library(toxEval)
-library(dplyr)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)AOP_crosswalk <- read.csv(file.path(path_to_tox, "AOP_crosswalk.csv"), stringsAsFactors = FALSE)AOP_summaries <- chemicalSummary %>%
- left_join(select(AOP_crosswalk,
- endPoint=Component.Endpoint.Name,
- AOP_id = AOP..,
- AOP_title = AOP.Title), by="endPoint") %>%
- filter(!is.na(AOP_id)) %>%
- group_by(shortName,date,AOP_id, AOP_title) %>%
- summarise(sumEAR = sum(EAR, na.rm = TRUE)) %>%
- group_by(shortName, AOP_id, AOP_title) %>%
- summarise(maxEAR = max(sumEAR, na.rm = TRUE)) %>%
- group_by(AOP_id, AOP_title) %>%
- summarise(sum_of_maxes = sum(maxEAR)) %>%
- arrange(desc(sum_of_maxes))
-
-threshold <- 1
-
-
-kable(filter(AOP_summaries, sum_of_maxes> threshold))| AOP_id | -AOP_title | -sum_of_maxes | -
|---|---|---|
| 36 | -Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis | -13.803051 | -
| 200 | -Estrogen receptor activation leading to breast cancer | -10.637468 | -
| 112 | -Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat) | -10.461258 | -
| 167 | -Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse | -10.461258 | -
| 29 | -Estrogen receptor agonism leading to reproductive dysfunction | -9.997717 | -
| 52 | -ER agonism leading to skewed sex ratios due to altered sexual differentiation in males | -9.984612 | -
| 53 | -ER agonism leading to reduced survival due to renal failure | -9.984612 | -
| 30 | -Estrogen receptor antagonism leading to reproductive dysfunction | -9.316480 | -
| 72 | -Epigenetic Modification of PPARg leading to Adipogenesis | -9.228469 | -
| 58 | -NR1I3 (CAR) suppression leading to hepatic steatosis | -6.127016 | -
| 34 | -LXR activation to liver steatosis | -4.808706 | -
| 163 | -PPARgamma activation leading to sarcomas in rats, mice and hamsters | -4.604469 | -
| 206 | -peroxisome proliferatory-activated receptor gamma inactivation leading to lung fibrosis | -4.578386 | -
| 165 | -Antiestrogen activity leading to ovarian adenomas and granular cell tumors in the mouse | -1.141249 | -
KE_summaries <- chemicalSummary %>%
- left_join(select(AOP_crosswalk,
- endPoint=Component.Endpoint.Name,
- KE_id = KE.,
- KE_title = Key.Event.Name), by="endPoint") %>%
- filter(!is.na(KE_id)) %>%
- group_by(shortName,date,KE_id, KE_title) %>%
- summarise(sumEAR = sum(EAR, na.rm = TRUE)) %>%
- group_by(shortName, KE_id, KE_title) %>%
- summarise(maxEAR = max(sumEAR, na.rm = TRUE)) %>%
- group_by(KE_id, KE_title) %>%
- summarise(sum_of_maxes = sum(maxEAR)) %>%
- arrange(desc(sum_of_maxes))
-
-threshold <- 1
-
-kable(filter(KE_summaries, sum_of_maxes> threshold))| KE_id | -KE_title | -sum_of_maxes | -
|---|---|---|
| 111 | -Agonism, Estrogen receptor | -29.953836 | -
| 228 | -peroxisome proliferator activated receptor promoter demethylation | -13.872001 | -
| 748 | -Increased, Estrogen receptor (ER) activity | -10.461258 | -
| 1064 | -prepubertal increase, Estrogen receptor (ER) activity | -10.461258 | -
| 1181 | -Activation, Estrogen receptor | -10.461258 | -
| 112 | -Antagonism, Estrogen receptor | -9.316480 | -
| 1028 | -Activation of specific nuclear receptors, PPAR-gamma activation | -9.208938 | -
| 231 | -Decreased, PPAR-alpha activation | -4.601017 | -
| 232 | -Decreased, PPAR-beta activation | -4.601017 | -
| 233 | -Decreased, PPAR-gamma activation | -4.601017 | -
| 1270 | -Inactivation of PPARγ | -4.574529 | -
| 177 | -N/A, Mitochondrial dysfunction 1 | -2.951766 | -
| 456 | -Suppression, Constitutive androstane receptor, NR1l3 | -1.975296 | -
| 245 | -Activation, PXR/SXR | -1.427182 | -
| 1046 | -Suppression, Estrogen receptor (ER) activity | -1.141249 | -
non_AOP_summaries <- chemicalSummary %>%
- anti_join(select(AOP_crosswalk,
- endPoint=Component.Endpoint.Name), by="endPoint") %>%
- group_by(shortName,date, endPoint) %>%
- summarise(sumEAR = sum(EAR, na.rm = TRUE)) %>%
- group_by(shortName, endPoint) %>%
- summarise(maxEAR = max(sumEAR, na.rm = TRUE)) %>%
- group_by(endPoint) %>%
- summarise(sum_of_maxes = sum(maxEAR)) %>%
- arrange(desc(sum_of_maxes))
-
-threshold <- 1
-
-kable(filter(non_AOP_summaries, sum_of_maxes> threshold))| endPoint | -sum_of_maxes | -
|---|---|
| CLD_CYP1A1_6hr | -7.443261 | -
| CLD_CYP1A2_6hr | -6.968355 | -
| ACEA_T47D_80hr_Positive | -1.003980 | -
Digging down:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-library(stringi)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-plot_tox_boxplots(chemicalSummary, category = "Biological")
Now let’s just look at Nuclear Receptors:
-CAS_NR <- unique(chemicalSummary$CAS[chemicalSummary$Bio_category == "Nuclear Receptor"])
-
-chem_data_NR <- filter(tox_list$chem_data, CAS %in% CAS_NR)
-chem_info_NR <- filter(tox_list$chem_info, CAS %in% CAS_NR)
-
-filtered_ep <- filter_groups(cleaned_ep, groupCol = "intended_target_gene_symbol")
-
-chemicalSummary <- get_chemical_summary(tox_list,ACClong, filtered_ep,
- chem.data = chem_data_NR,
- chem.info = chem_info_NR)
-
-plot_tox_boxplots(chemicalSummary, category = "Biological")
And now…we dig down to just “ESR1”:
-CAS_PTEN <- unique(chemicalSummary$CAS[chemicalSummary$Bio_category == "ESR1"])
-
-chem_data_PTEN <- filter(chem_data_NR, CAS %in% CAS_PTEN)
-chem_info_PTEN <- filter(chem_info_NR, CAS %in% CAS_PTEN)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep,
- chem.data = chem_data_PTEN,
- chem.info = chem_info_PTEN)
-plot_tox_endpoints(chemicalSummary)
The toxEval R-package includes a set of functions to analyze, visualize, and organize measured concentration data as it relates to ToxCast data. These functions allow great flexibly for exploring the potential biological affects of measured chemicals. Also included in the package is a browser-based application made from the shiny R-package. There is less customization from the application, but it is a convenient way to explore the data.
This vignette describes the process to prepare the data and use the shiny application. There is also a section on installing and updating the package, since it is not on CRAN.
+The U.S. EPA’s Toxicity Forecaster ToxCast is a database with thousands of chemicals that can give biological relevance to measured concentrations. The toxEval package attempts to simplify the workflow for exploring data as it relates to the benchmark data. By default, the workflow uses the ToxCast information, but there is also a way to provide alternative benchmarks. This could be useful for comparing ToxCast results with other toxicity benchmark databases.
When using the ToxCast endPoints for analysis, it will be important to have at least a minimal understanding of what ToxCast data is, and which ToxCast data is relevant to your study. There are many useful resources here. There is also a tool called the TosCast Dashboard that has a wealth of information on ToxCast data.
+So what are we doing? First, we calculate an Exposure-Activity Ratio (EAR) for each measurement. Then we can explore the EARs based on a wide variety of groupings to explore the data.
+A Exposure-Activity Ratio (EAR) is the measured concentration divided by a concentration that was determined to cause some activity. The ToxCast database (as provided in the current version of toxEval) provides 991 “endPoints” for more than 6575 chemicals. Each endPoint is a single test that was done to detect some form of activity.
In order to get consistent EAR results, it is important to use the correct units. The toxEval package assumes all measured concentration is repored in micrograms per liter (ug/L). ToxCast data is reported in log(uM), so the toxEval package automatically performs the unit conversion.
ToxCast uses high-throughput assays to create does-response curves for each of these chemical/endPoint combinations. Activity concentration at cutoff (ACC) values of these does response curves are provided in the toxEval package. ACC was choosen over the chemical-specific half-maximal activity concentration (AC50) as described in Blackwell 2017.
An endPoint is “associated with the perturbation of specific biological processes identified for the confirmation or monitoring of predicted site-specific hazards” Blackwell 2017. That means a very specific action was tested, and concentration at which that activity was observed was determined. For ToxCast endPoints, that is via the ACC value in a dose response curve.
+Often, a single endPoint is too specific to be of much interest. ToxCast has provided tables that group individual endPoints into more generalized categories. The grouping summary table is included in toxEval and can be explored via the endPointInfo data:
library(toxEval)
+endPointInfo <- endPointInfoSee the help file ?endPointInfo for specifics on how the table was downloaded.
Throughout the toxEval analysis, there are graphing and table functions that will summarize EARs based on either “Biological” groupings (as defined by a group of endPoints) or “Chemical Class” groupings (as defined by a group of chemicals).
The default grouping of ToxCast endPoints is “intended_target_family”, but depending on the analysis, it may be more appropriate to use other grouping categories.
+The data should be prepared in a Microsoft ™ Excel file. There are 3 mandatory sheets (also known as tabs), and 2 optional sheets. The sheets should appear like this (although the order is not important):
+
+Organize Sheets
+Each sheet has mandatory columns; the order of the columns is not important, but the names of the columns is important. Additional columns can be included but will be ignored. The top row of each sheet should contain the column names (headers), no rows should be initially skipped, and no titles or comment rows should precede the data.
+The “Data” tab needs to have at a minimum 4 columns: “CAS”, “SiteID”, “Value”, and “Sample Date”. They can be in any order, but the first row of sheet should be the header (column names).
+CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose “Format Cells”, and choose “Text” as the category to assure they retain their format. The unique CAS values in this column must match with the CAS values in the “Chemicals” sheet.
SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique “SiteID” values must match the “SiteID” values in the “Sites” sheet. There is no requirements on formatting for this column.
Value: This column is numeric. It should be the concentration of the chemical in \(\mu\)g/L.
Sample Date: Currently this column only serves to identify unique samples. Dates and times as formatted in Excel, as well as integer values are acceptable. The actual date and times are not currently used to perform any time series analysis.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
As an example, the first several rows of a minimal example would look like this:
+
+Example Data
+The “Chemicals” tab needs to have at a minimum 2 columns: “CAS” and “Class”. They can be in any order, but the first row of sheet should be the header (column names).
+CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose “Format Cells”, and choose “Text” as the category to assure they retain their format. The unique CAS values in this column must match with the CAS values in the “Data” sheet.
Class: A character column defining the class of chemicals. Most toxEval functions will allow groupings by either chemical, class (as defined here), or biological grouping.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
The “Sites” tab needs to have at a minimum 4 columns: “SiteID”, “Short Name”, “dec_lon”, and “dec_lat”. They can be in any order, but the first row of sheet should be the header (column names).
+SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique “SiteID” values must match the “SiteID” values in the “Data” sheet. There is no requirements on formatting for this column.
Short Name: This character column is the name associated with the SiteID that will be used in the graphs and tables. The names should be sufficiently descriptive, but as short as possible to optimize the area on the graph devoted to the data and not the labels.
dec_lon: This numeric column is the decimal longitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most toxEval functions will not require this column.
dec_lat: This numeric column is the decimal latitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most toxEval functions will not require this column.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
The “Exclude” tab is an optional tab. If used, the “Exclude” tab needs to have at a minimum 2 columns: “CAS” and “endPoint”. They can be in any order, but the first row of sheet should be the header (column names).
+endPoint: A character column define a specific ToxCast endpoint to ignore. IF the “CAS” in the corresponding row is empty, the endPoint will be completely excluded from the toxEval analysis. For example, maybe it was decided that TOX21_p53_BLA_p3_ratio was not an appropriate endPoint to consider in the analysis. We can exclude all TOX21_p53_BLA_p3_ratio endPoints by adding it to the Exclude tab. IF the “CAS” in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. IF the “endPoint” in the corresponding row is empty, the chemical will be completely excluded from the toxEval analysis. For example, maybe it was discovered that DEET was contaminated in the sampling method. We can exclude all DEET measurements by adding 134-62-3 to the Exclude tab. IF the “endPoint” in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
There could be a variety of reasons to exclude chemical/endpoint combinations. This will be discussed further in section TODO: XXXXXXXXX.
+Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
The “Benchmarks” tab is an optional tab. The benchmark tab can be used to over-ride the ToxCast endpoints. For example, there may be a need to perform similar toxEval analysis using EPA benchmarks instead of ToxCast endpoints.
For the functions to work seamlessly with all toxEval functions, the “Benchmarks” tab needs to have at a minimum 5 columns: “CAS”, “Chemical”, “endPoint”, “Value”, and “groupCol”. They can be in any order, but the first row of sheet should be the header (column names).
CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry.
Chemical: A character column defining the name of the chemicals.
endPoint: A character column naming the benchmark
Value: The concentration (in identical units as what is reported in the “Data” tab) of the benchmark.
groupCol: A character column that groups endpoints. This is analogous to the Biological groupings from the ToxCast analysis.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
Using the detection levels as concentrations:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-library(grid)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-# Substitute max LDL or MDL for actual values:
-
-tox_list$chem_data <- tox_list$chem_data %>%
- left_join(select(tox_list$chem_info,
- CAS,
- MDL = `Maximum method detection level`,
- LDL = `Maximum laboratory reporting level`),
- by="CAS") %>%
- rowwise() %>%
- mutate(Value = max(MDL, LDL, na.rm = TRUE)) %>%
- select(SiteID, `Sample Date`, CAS, Value) %>%
- distinct()
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-#Trim some names:
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-# Basically...need to swap endpoint and site to take advantage of plot_tox_boxplots code
-
-chemicalSummary$site <- chemicalSummary$endPoint
-
-plot_DL <- plot_tox_boxplots(chemicalSummary, "Chemical")
-
-plot_DL
-grid.text("# Endpoints:",
- x = unit(.375, "npc"),
- y = unit(.992, "npc"), gp=gpar(fontsize=7))
Using the detection levels as concentrations:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-#Trim some names:
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-# Order the Great Lakes:
-tox_list$chem_site$site_grouping <- factor(tox_list$chem_site$site_grouping,
- levels=c("Lake Superior",
- "Lake Michigan",
- "Lake Huron",
- "Lake Erie",
- "Lake Ontario"))
-
-# Order sites:
- sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal",
- "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns",
- "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI",
- "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc",
- "Manistee","Fox","Oconto","Peshtigo","Menominee",
- "Indian","Cheboygan","Ford","Escanaba","Manistique",
- "ThunderBay","AuSable","Rifle","Saginaw","BlackMI",
- "Clinton","Rouge","HuronMI","Raisin","Maumee",
- "Portage","Sandusky","HuronOH","Vermilion","BlackOH",
- "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda",
- "Genesee","Oswego","BlackNY","Oswegatchie","Grass",
- "Raquette","StRegis")
-
- tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`,
- levels = sitesOrdered)
-
-
- plot_tox_heatmap(chemicalSummary,
- tox_list$chem_site,
- category = "Chemical")
Using the detection levels as concentrations:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-#Trim some names:
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-tableData <- chemicalSummary %>%
- group_by(site, date, chnm) %>%
- summarize(sumEAR = sum(EAR)) %>%
- group_by(site, chnm) %>%
- summarize(meanEAR = max(sumEAR)) %>%
- group_by(chnm) %>%
- summarize(nSites = sum(meanEAR > 10^-3)) %>%
- data.frame() %>%
- arrange(desc(nSites)) %>%
- filter(nSites > 1)
-
-tableData$chnm <- factor(tableData$chnm, levels = tableData$chnm)
-
-chemPlot <- ggplot(tableData)+
- geom_bar(aes(x=chnm, y=nSites),stat = "identity",fill = "steelblue") +
- theme_bw() +
- xlab("") +
- ylab("Number of Sites\n with EARmax > 0.001") +
- theme(axis.text.x = element_text( angle = 90,vjust=0.5,hjust = 1))
-
-chemPlot
Using the detection levels as concentrations:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-# Order the Great Lakes:
-tox_list$chem_site$site_grouping <- factor(tox_list$chem_site$site_grouping,
- levels=c("Lake Superior",
- "Lake Michigan",
- "Lake Huron",
- "Lake Erie",
- "Lake Ontario"))
-
-# Order sites:
- sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal",
- "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns",
- "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI",
- "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc",
- "Manistee","Fox","Oconto","Peshtigo","Menominee",
- "Indian","Cheboygan","Ford","Escanaba","Manistique",
- "ThunderBay","AuSable","Rifle","Saginaw","BlackMI",
- "Clinton","Rouge","HuronMI","Raisin","Maumee",
- "Portage","Sandusky","HuronOH","Vermilion","BlackOH",
- "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda",
- "Genesee","Oswego","BlackNY","Oswegatchie","Grass",
- "Raquette","StRegis")
-
- tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`,
- levels = sitesOrdered)
-
- plot_tox_heatmap(chemicalSummary,
- tox_list$chem_site,
- category = "Biological",
- manual_remove = "Undefined")
Using the detection levels as concentrations:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-library(stringi)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-chemicalSummary <- chemicalSummary %>%
- left_join(select(endPointInfo,
- endPoint=assay_component_endpoint_name,
- family = intended_target_family,
- subFamily = intended_target_family_sub), by="endPoint")
-
-graphData <- chemicalSummary %>%
- filter(!is.na(subFamily)) %>%
- group_by(site, Bio_category, subFamily, date) %>%
- summarize(sumEAR = sum(EAR)) %>%
- group_by(site, Bio_category, subFamily) %>%
- summarize(meanEAR = max(sumEAR)) %>%
- data.frame() %>%
- mutate(subFamily = stri_trans_totitle(subFamily))
-
-orderSub <- graphData %>%
- group_by(Bio_category) %>%
- summarise(median = median(meanEAR[meanEAR != 0])) %>%
- data.frame() %>%
- arrange(desc(median))
-
-orderGroups <- graphData %>%
- group_by(subFamily, Bio_category) %>%
- summarise(median = quantile(meanEAR[meanEAR != 0],0.5)) %>%
- data.frame() %>%
- mutate(Bio_category = factor(Bio_category, levels=orderSub$Bio_category)) %>%
- arrange(Bio_category, desc(median))
-
-orderedSub <- rev(orderGroups$subFamily)[rev(orderGroups$subFamily) %in% unique(graphData$subFamily)]
-orderedSub <- unique(orderedSub)
-
-graphData$subFamily <- factor(graphData$subFamily,
- levels = orderedSub)
-
-graphData$Bio_category <- factor(graphData$Bio_category,
- levels = orderSub$Bio_category)
-
-cbValues <- c("#DCDA4B","#999999","#00FFFF","#CEA226","#CC79A7","#4E26CE",
- "#FFFF00","#78C15A","#79AEAE","#FF0000","#00FF00","#B1611D",
- "#FFA500","#F4426e", "#4286f4","red","pink")
-
-countNonZero <- graphData %>%
- group_by(subFamily) %>%
- summarise(nonZero = as.character(length(unique(site[meanEAR>0])))) %>%
- data.frame()
-
-subPlot <- ggplot(graphData)+
- scale_y_log10("Maximum EAR Per Site",labels=fancyNumbers)+
- geom_boxplot(aes(x=subFamily, y=meanEAR,fill = Bio_category),
- lwd=0.1,outlier.size=1) +
- coord_flip() +
- theme_bw() +
- xlab("") +
- theme(plot.background = element_rect(fill = "transparent",colour = NA),
- axis.text = element_text(size=8, color = "black"),
- axis.text.y = element_text(vjust = 0.2),
- axis.text.x = element_text(vjust = 0, margin = margin(-0.5,0,0,0)),
- axis.title = element_text(size=10)) +
- scale_fill_manual(values = cbValues, drop=TRUE) +
- guides(fill=guide_legend(ncol=6)) +
- theme(legend.position="bottom",
- legend.justification = "left",
- legend.background = element_rect(fill = "transparent", colour = "transparent"),
- legend.title=element_blank(),
- legend.text = element_text(size=8),
- legend.key.height = unit(1,"line"))
-
-plot_info <- ggplot_build(subPlot)
-layout_stuff <- plot_info$layout
-
-if(packageVersion("ggplot2") >= "2.2.1.9000"){
- xmin <- 10^(layout_stuff$panel_scales_y[[1]]$range$range[1])
-} else {
- xmin <- 10^(layout_stuff$panel_ranges[[1]]$x.range[1])
-}
-
-
-subPlot <- subPlot +
- geom_text(data=countNonZero, aes(x=subFamily, y=xmin,label=nonZero),size=3)
-
-subPlot
The “Xenobiotic Metabolism” has multiple families because we switched some by hand in the clean_endPoint_info function.
- - - - - - - - diff --git a/inst/doc/SI7_ClassStacks.R b/inst/doc/SI7_ClassStacks.R deleted file mode 100644 index 98e868ce..00000000 --- a/inst/doc/SI7_ClassStacks.R +++ /dev/null @@ -1,70 +0,0 @@ -## ----setup, include=FALSE--------------------------------- -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 10) - -## --------------------------------------------------------- -library(toxEval) -library(dplyr) -library(ggplot2) -library(grid) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", - "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", - "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", - "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", - "Manistee","Fox","Oconto","Peshtigo","Menominee", - "Indian","Cheboygan","Ford","Escanaba","Manistique", - "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", - "Clinton","Rouge","HuronMI","Raisin","Maumee", - "Portage","Sandusky","HuronOH","Vermilion","BlackOH", - "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", - "Genesee","Oswego","BlackNY","Oswegatchie","Grass", - "Raquette","StRegis") - -chem_site <- tox_list$chem_site %>% - mutate(`Short Name` = factor(`Short Name`, levels = sitesOrdered)) %>% - mutate(site_grouping = factor(site_grouping, levels = c("Lake Superior", - "Lake Michigan", - "Lake Huron", - "Lake Erie", - "Lake Ontario"))) - -for(class in unique(chemicalSummary$Class)){ - sub_class <- filter(chemicalSummary, Class %in% class) - - upperPlot <- plot_tox_stacks(sub_class, - chem_site, - category = "Chemical") - upperPlot <- upperPlot + - ggtitle(class) - - print(upperPlot) - grid.text("# Samples:", - x = unit(.03, "npc"), - y = unit(.205, "npc"), gp=gpar(fontsize=7)) -} - - - - diff --git a/inst/doc/SI7_ClassStacks.Rmd b/inst/doc/SI7_ClassStacks.Rmd deleted file mode 100644 index 0594b37e..00000000 --- a/inst/doc/SI7_ClassStacks.Rmd +++ /dev/null @@ -1,87 +0,0 @@ ---- -title: "SI7: Class" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI7} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 10) -``` - - - - - -```{r } -library(toxEval) -library(dplyr) -library(ggplot2) -library(grid) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", - "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", - "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", - "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", - "Manistee","Fox","Oconto","Peshtigo","Menominee", - "Indian","Cheboygan","Ford","Escanaba","Manistique", - "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", - "Clinton","Rouge","HuronMI","Raisin","Maumee", - "Portage","Sandusky","HuronOH","Vermilion","BlackOH", - "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", - "Genesee","Oswego","BlackNY","Oswegatchie","Grass", - "Raquette","StRegis") - -chem_site <- tox_list$chem_site %>% - mutate(`Short Name` = factor(`Short Name`, levels = sitesOrdered)) %>% - mutate(site_grouping = factor(site_grouping, levels = c("Lake Superior", - "Lake Michigan", - "Lake Huron", - "Lake Erie", - "Lake Ontario"))) - -for(class in unique(chemicalSummary$Class)){ - sub_class <- filter(chemicalSummary, Class %in% class) - - upperPlot <- plot_tox_stacks(sub_class, - chem_site, - category = "Chemical") - upperPlot <- upperPlot + - ggtitle(class) - - print(upperPlot) - grid.text("# Samples:", - x = unit(.03, "npc"), - y = unit(.205, "npc"), gp=gpar(fontsize=7)) -} - - - -``` \ No newline at end of file diff --git a/inst/doc/SI7_ClassStacks.html b/inst/doc/SI7_ClassStacks.html deleted file mode 100644 index 5f755c70..00000000 --- a/inst/doc/SI7_ClassStacks.html +++ /dev/null @@ -1,143 +0,0 @@ - - - - - - - - - - - - - - -library(toxEval)
-library(dplyr)
-library(ggplot2)
-library(grid)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal",
- "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns",
- "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI",
- "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc",
- "Manistee","Fox","Oconto","Peshtigo","Menominee",
- "Indian","Cheboygan","Ford","Escanaba","Manistique",
- "ThunderBay","AuSable","Rifle","Saginaw","BlackMI",
- "Clinton","Rouge","HuronMI","Raisin","Maumee",
- "Portage","Sandusky","HuronOH","Vermilion","BlackOH",
- "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda",
- "Genesee","Oswego","BlackNY","Oswegatchie","Grass",
- "Raquette","StRegis")
-
-chem_site <- tox_list$chem_site %>%
- mutate(`Short Name` = factor(`Short Name`, levels = sitesOrdered)) %>%
- mutate(site_grouping = factor(site_grouping, levels = c("Lake Superior",
- "Lake Michigan",
- "Lake Huron",
- "Lake Erie",
- "Lake Ontario")))
-
-for(class in unique(chemicalSummary$Class)){
- sub_class <- filter(chemicalSummary, Class %in% class)
-
- upperPlot <- plot_tox_stacks(sub_class,
- chem_site,
- category = "Chemical")
- upperPlot <- upperPlot +
- ggtitle(class)
-
- print(upperPlot)
- grid.text("# Samples:",
- x = unit(.03, "npc"),
- y = unit(.205, "npc"), gp=gpar(fontsize=7))
-}
Get data ready:
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(DT)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-#Trim some names:
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-
-file_name <- "AOP_crosswalk.csv"
-full_path <- file.path(path_to_tox, file_name)
-
-AOP_crosswalk <- read.csv(full_path, stringsAsFactors = FALSE)
-
-chemicalSummary <- chemicalSummary %>%
- left_join(select(endPointInfo,
- endPoint=assay_component_endpoint_name,
- subFamily=intended_target_family_sub,
- gene_symbol=intended_target_gene_symbol), by="endPoint") %>%
- left_join(select(tox_list$chem_info, CAS, `Chemical Name`), by="CAS")tableData <- chemicalSummary %>%
- rename(Chemical=`Chemical Name`,
- Family=Bio_category) %>%
- group_by(site, endPoint, Family, subFamily, gene_symbol, Chemical)
-
-max_Samples <- tableData %>%
- summarize(sumEAR = sum(EAR)) %>% #Sum per date
- slice(which.max(sumEAR)) %>% # Gets max per date
- filter(sumEAR > 0) %>%
- data.frame() %>%
- spread(Chemical, sumEAR) %>%
- arrange(site, Family, subFamily, gene_symbol) %>%
- select(site, Family, subFamily, gene_symbol,endPoint, everything()) %>%
- mutate(maxSample = rowSums(.[-1:-5], na.rm = TRUE)) %>%
- select(site, Family, subFamily, gene_symbol, endPoint, maxSample)
-
-tableData <- tableData %>%
- summarize(maxEAR = max(EAR)) %>%
- filter(maxEAR > 0) %>%
- data.frame() %>%
- spread(Chemical, maxEAR) %>%
- arrange(site, Family, subFamily, gene_symbol) %>%
- select(site, Family, subFamily, gene_symbol,endPoint, everything()) %>%
- left_join(select(AOP_crosswalk,
- endPoint=Component.Endpoint.Name,
- AOP_id = AOP..,
- AOP_title = AOP.Title), by="endPoint") %>%
- left_join(max_Samples, by=c("site", "Family", "subFamily", "gene_symbol","endPoint")) %>%
- select(site, Family, subFamily, gene_symbol, endPoint, AOP_id, AOP_title, maxSample, everything())
-
-list_tables <- list()
-chem_site <- tox_list$chem_site
-
-for(i in 1:nrow(chem_site)){
-
- site <- chem_site$SiteID[i]
- site_name <- chem_site$`Short Name`[i]
- tableData_site <- tableData[tableData$site == site,]
- tableData_site <- Filter(function(x)!all(is.na(x)), tableData_site)
-
- list_tables[[2*i-1]] <- htmltools::tags$h3(site_name)
-
- if(nrow(tableData_site) > 0){
- tableData2 <- select(tableData_site, -endPoint, -Family, -subFamily, -gene_symbol, -AOP_id, -AOP_title, -site, -maxSample)
- tableData_site$nChems <- apply(tableData2, MARGIN = 1, function(x) sum(x>0, na.rm = TRUE))
- orderedCols <- tox_list$chem_info$`Chemical Name`[tox_list$chem_info$`Chemical Name` %in% names(tableData_site)]
-
- tableData_site <- tableData_site[,c("Family", "subFamily", "gene_symbol", "endPoint","AOP_id","AOP_title", "maxSample", "nChems", orderedCols)]
-
- dt_table <- datatable(tableData_site, rownames = FALSE,extensions = 'Buttons',
- options = list(dom = 'Bfrtip',
-
- buttons = list('colvis', list(
- extend = 'collection',
- buttons = list(list(extend='csv',
- filename = 'siteTable'),
- list(extend='excel',
- filename = 'siteTable'),
- list(extend='pdf',
- filename= 'siteTable')),
- text = 'Download')
- ))) %>%
- formatSignif(columns=c("maxSample",orderedCols), digits=3)
-
- list_tables[[2*i]] <- dt_table
- } else {
- list_tables[[2*i]] <- htmltools::tags$h3("EAR never > 0")
- }
-}htmltools::tagList(
- list_tables
-)Get data ready:
-library(readxl)
-library(toxEval)
+Basic Workflow
+03 April, 2018
+
+
+
+
+
+
+Load Data
+Once the data is prepared as described in the vignette: “Prepare Data”, it can be loaded into R using the create_toxEval function.
+library(toxEval)
path_to_tox <- system.file("extdata", package="toxEval")
file_name <- "OWC_data_fromSup.xlsx"
full_path <- file.path(path_to_tox, file_name)
-chem_data <- read_excel(full_path, sheet = "Data")
-chem_info <- read_excel(full_path, sheet = "Chemicals")
-chem_site <- read_excel(full_path, sheet = "Sites")
-exclusion <- read_excel(full_path, sheet = "Exclude")
-
-#Trim names and order for graph:
-chem_info$Class[chem_info$Class == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-chem_info$Class[chem_info$Class == "Detergent Metabolites"] <- "Detergent"
-chem_info$Class[chem_info$Class == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-chem_info$Class <- factor(chem_info$Class)
-
-ACClong <- get_ACC(chem_info$CAS)
+tox_list <- create_toxEval(full_path)
+The create_toxEval function will check that required columns are included, and give messages if there are unusual issues such as sites that don’t match up with data, or chemicals that don’t match up with chemical information.
+The vast majority of toxEval functions require a data frame described as chemicalSummary. The next section will walk through the options that are available in the workflow.
+
+
+Setup Analysis
+The next step for exploring the data in toxEval is to create a data frame that includes the EAR calculations for each measurement. Since one chemical may have many more than one “endPoint”, this data frame may be much larger then the “Data” tab in the Excel file. The general workflow for creating this data frame is:
+ACClong <- get_ACC(tox_list$chem_info$CAS)
ACClong <- remove_flags(ACClong)
+
cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-SI Table 2: Chemical Classes
+filtered_ep <- filter_groups(cleaned_ep)
+
+chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
+What’s going on with this workflow?
+
+get_ACC
+The get_ACC function will retrieve the ACC values for the specified CAS (chemicals). The data was originally downloaded for toxEval on October 2015 from ToxCast. The data were combined from files in the “INVITRODB_V2_LEVEL5” folder. At the time of toxEval package release, this information was found here in the “ToxCast & Tox21 Data Spreadsheet” data set. The ACC data frame provided in toxEval is in a wide format. The function get_ACC will convert it to a long format, and convert the native log(uM) to ug/L.
+
+
+remove_flags
+There are a set of flags that ToxCast includes with the ACC values. Depending on the scope of your analysis, you may want to include EARs with certain flags. EndPoints are removed based on their designated flag with the remove_flags function. The flags included in ToxCast, and the associated flagsShort value (used in the remove_flags function):
+
+
+
+Flags
+flagsShort
+
+
+
+
+Borderline active
+Borderline
+
+
+Only highest conc above baseline, active
+OnlyHighest
+
+
+Only one conc above baseline, active
+OneAbove
+
+
+Noisy data
+Noisy
+
+
+Hit-call potentially confounded by overfitting
+HitCall
+
+
+Gain AC50 < lowest conc & loss AC50 < mean conc
+GainAC50
+
+
+Biochemical assay with < 50% efficacy
+Biochemical
+
+
+
+
+
+clean_endPoint_info
+The clean_endPoint_info function cleans up the endPointInfo table from ToxCast. Filtering based on Blackwell. Specifically, this function hard-codes in the removal of endPoints that are ATG sources with signal loss, and NVS with signal gain. Also, this function adds some additional categories to intended_target_family and intended_target_family_sub as described in the paper linked above.
+
+
+filter_groups
+This function takes the data frame from ‘endPointInfo’ and filters the endpoints in 3 steps. First, the user specifies the “groupCol” argument, which is a column header from endPointInfo. Second, the user specifies the assays to use. Finally, the user can also choose to remove specific group from the category. The default is to remove “Background Measurement” and “Undefined”, but it is a good idea to check if other groups may not be relevant to the study.
+The default category (“groupCol”) is “intended_target_family”. Depending on the study, other categories may be more relavant. To discover to options, look at the column names in endPointInfo:
+names(endPointInfo)
+By default, the BioSeek set of assays are removed. The list of assays and their short names are here:
+
+
+
+Assay Name
+Short Name
+
+
+
+
+Apredica
+APR
+
+
+Attagene
+ATG
+
+
+BioSeek
+BSK
+
+
+NovaScreen
+NVS
+
+
+Odyssey Thera
+OT
+
+
+Toxicity Testing
+TOX21
+
+
+CEETOX
+CEETOX
+
+
+CLD
+CLD
+
+
+TANGUAY
+TANGUAY
+
+
+NHEERL_PADILLA
+NHEERL_PADILLA
+
+
+NCCT_SIMMONS
+NCCT_SIMMONS
+
+
+ACEA Biosciences
+ACEA
+
+
+
+
+
+get_chemical_summary
+Finally, the function get_chemical_summary takes the measured user data from the output of create_toxEval, and joins the data with the endPoint information provided by ToxCast, as filtered by the functions described above.
+
+
+Custom configuration
+Let’s say we want to trim some names to make the labels more readable in graphs. Here we clean up some chemical class labels:
+#Trim some names:
+levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial"
+levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent"
+levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance"
+And here, we convert the site lists to factors. This will make any site graph in an order that we define. Here, we take Great Lakes sites and make the order up-to-down stream:
+#Ordering the sites to flow "downstream" of the Great Lakes:
+sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal",
+ "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns",
+ "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI",
+ "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc",
+ "Manistee","Fox","Oconto","Peshtigo","Menominee",
+ "Indian","Cheboygan","Ford","Escanaba","Manistique",
+ "ThunderBay","AuSable","Rifle","Saginaw","BlackMI",
+ "Clinton","Rouge","HuronMI","Raisin","Maumee",
+ "Portage","Sandusky","HuronOH","Vermilion","BlackOH",
+ "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda",
+ "Genesee","Oswego","BlackNY","Oswegatchie","Grass",
+ "Raquette","StRegis")
+
+ tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`,
+ levels = sitesOrdered)
+
+lakes_ordered <- c("Lake Superior",
+ "Lake Michigan",
+ "Lake Huron",
+ "Lake Erie",
+ "Lake Ontario")
+
+tox_list$chem_site$site_grouping <- factor(tox_list$chem_site$site_grouping,
+ levels=lakes_ordered)
+
+
+
+Visualize Data
+
+plot_tox_boxplots
+The plot_tox_boxplots function
+library(grid)
+plot_tox_boxplots(chemicalSummary, "Biological")
+grid.text("# Sites w/ Detections:",
+ x = unit(.22, "npc"),
+ y = unit(.994, "npc"), gp=gpar(fontsize=7))
+
+# Other options:
+# plot_tox_boxplots(chemicalSummary, "Chemical Class")
+# plot_tox_boxplots(chemicalSummary, "Chemical")
+The graph shows a slightly different result for a single site. First, let’s set up a subset of data that we will use throughout this document to show a single site. We’ll use the Maumee River data.
library(dplyr)
-library(tidyr)
-library(DT)
-
-chem_info_SI <- chem_info %>%
- select(`OWC Class`=Class,
- `Compound Name` = `Chemical Name`,
- `CAS Registry Number` = CAS,
- EEF_max_in.vitro_or_in.vivo,
- AqT_EPA_acute,
- AqT_EPA_chronic,
- AqT_other_acute) %>%
- mutate(Units = "ug/l")
-
-# write.csv(chem_info_SI, file="chem_info_SI.csv", row.names = FALSE, na = "")
-
-chem_info_SI <- chem_info_SI %>%
- mutate(EEF_max_in.vitro_or_in.vivo = as.numeric(EEF_max_in.vitro_or_in.vivo),
- AqT_EPA_acute = as.numeric(AqT_EPA_acute),
- AqT_EPA_chronic = as.numeric(AqT_EPA_chronic),
- AqT_other_acute = as.numeric(AqT_other_acute))
-
-datatable(chem_info_SI,
- rownames = FALSE,
- extensions = 'Buttons',
- options = list(
- dom = 'Bfrtip',
- buttons = list('colvis', list(
- extend = 'collection',
- buttons = list(list(extend='csv',
- filename = 'siteTable'),
- list(extend='excel',
- filename = 'siteTable'),
- list(extend='pdf',
- filename= 'fullTable')),
- text = 'Download')
- ))) %>%
- formatRound(columns=c('EEF_max_in.vitro_or_in.vivo',
- 'AqT_EPA_acute',
- 'AqT_EPA_chronic',
- 'AqT_other_acute'), digits=3)
-
-
+
+maumee <- filter(chemicalSummary, shortName == "Maumee")
+maumee_site <- filter(tox_list$chem_site, `Short Name` == "Maumee")
+Now, we’ll run plot_tox_boxplots on that single site. The difference in the output is that instead of listing the number of sites, it lists the number of unique chemical/endpoint combinations used to create the box plot. We’ve also included the code to add a custom title to the output:
+library(ggplot2)
+
+maumee_plot <- plot_tox_boxplots(maumee, "Biological")
+maumee_plot <- maumee_plot +
+ ggtitle(maumee_site$Fullname[1])
+print(maumee_plot)
+grid.text("# Unique Chemical/endPoints:",
+ x = unit(.225, "npc"),
+ y = unit(.962, "npc"), gp=gpar(fontsize=7))
+
+
+
+plot_tox_boxplots
+plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Biological")
+grid.text("# Samples:",
+ x = unit(.03, "npc"),
+ y = unit(.205, "npc"), gp=gpar(fontsize=7))
+
+# More options:
+# plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Chemical Class")
+# plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Chemical", include_legend = FALSE)
+The graph shows a slightly different result for a single site:
+maumee_plot_stack <- plot_tox_stacks(maumee, maumee_site,"Biological")
+
+maumee_plot_stack <- maumee_plot_stack +
+ ggtitle(maumee_site$Fullname[1])
+print(maumee_plot_stack)
+
+
+
+plot_tox_heatmap
+plot_tox_heatmap(chemicalSummary,
+ tox_list$chem_site,
+ category = "Biological")
+
+# More options:
+# plot_tox_heatmap(chemicalSummary, tox_list$chem_site, category = "Chemical Class")
+# plot_tox_heatmap(chemicalSummary, tox_list$chem_site, category = "Chemical")
+
+
+plot_tox_endpoints
+plot_tox_endpoints(chemicalSummary, filterBy = "Cell Cycle")
+grid.text("# Sites w/ Detections:",
+ x = unit(.38, "npc"),
+ y = unit(.995, "npc"), gp=gpar(fontsize=7))
+
+# More options:
+# plot_tox_endpoints(chemicalSummary, category = "Chemical Class", filterBy = "PAHs")
+# plot_tox_endpoints(chemicalSummary, category = "Chemical", filterBy = "Atrazine")
+
+library(DT)
+options(DT.options = list(pageLength = 5))
+
+table_tox_rank(chemicalSummary, category = "Biological")# More options:
+# table_tox_rank(chemicalSummary, category = "Chemical Class")
+# table_tox_rank(chemicalSummary, category = "Chemical")The tables show slightly different results for a single site:
+table_tox_rank(maumee, category = "Biological")table_tox_sum(chemicalSummary, category = "Biological")# More options:
+# table_tox_sum(chemicalSummary, category = "Chemical Class")
+# table_tox_sum(chemicalSummary, category = "Chemical")The tables show slightly different results for a single site:
+table_tox_sum(maumee, category = "Biological")table_endpoint_hits(chemicalSummary, category = "Biological")# More options:
+# table_endpoint_hits(chemicalSummary, category = "Chemical Class")
+# table_endpoint_hits(chemicalSummary, category = "Chemical")The tables show slightly different results for a single site. Instead of the number of sites with hits above a threshold, it is now the number of samples.
+table_endpoint_hits(maumee, category = "Biological")intended_target <- select(endPointInfo, intended_target_family, intended_target_family_sub, endPoint = assay_component_endpoint_name, source = assay_source_long_name) %>%
- right_join(select(filtered_ep, endPoint), by = "endPoint") %>%
- arrange(intended_target_family, intended_target_family_sub)
-
-intended_target$intended_target_family_sub["Zebrafish" == intended_target$intended_target_family] <- "Zebrafish"
-
-intended_target <- intended_target %>%
- rename(`Intended Target Family`=intended_target_family,
- `Intended Target Family Sub-Family` = intended_target_family_sub) %>%
- data.frame()
-
-datatable(intended_target,
- rownames = FALSE,
- extensions = 'Buttons',
- options = list(
- dom = 'Bfrtip',
- buttons = list('colvis', list(
- extend = 'collection',
- buttons = list(list(extend='csv',
- filename = 'siteTable'),
- list(extend='excel',
- filename = 'siteTable'),
- list(extend='pdf',
- filename= 'fullTable')),
- text = 'Download')
- )))# write.csv(intended_target, file="intended_target.csv", row.names = FALSE, na = "") Table 4: Endpoint Counts
-ACC <- ACCtable_tox_endpoint(chemicalSummary, category = "Biological")# More options:
+# table_tox_endpoint(chemicalSummary, category = "Chemical Class")
+# table_tox_endpoint(chemicalSummary, category = "Chemical")The tables show slightly different results for a single site, showing the number of samples with hits (instead of number of sites).
+table_tox_endpoint(maumee, category = "Biological")# file_name <- "AOP_crosswalk.csv"
-# full_path <- file.path(path_to_tox, file_name)
-#
-# AOP_crosswalk <- read.csv(full_path, stringsAsFactors = FALSE)
-#
-# AOP_crosswalk <- select(AOP_crosswalk,
-# gene_symbol=Target.Gene.Symbol,
-# AOP=AOP.name)
-#
-# AOP <- data.frame(gene_symbol = unique(AOP_crosswalk$gene_symbol),
-# AOP = "",
-# stringsAsFactors = FALSE)
-# for(gene in AOP$gene_symbol){
-# AOP$AOP[AOP$gene_symbol %in% gene] <- paste(AOP_crosswalk$AOP[AOP_crosswalk$gene_symbol %in% gene],collapse = ", ")
-# }
-
-chemicalSummary <- get_chemical_summary(tox_list = NULL,
- ACClong,
- filtered_ep,
- chem.data = chem_data,
- chem.site = chem_site,
- chem.info = chem_info,
- exclusion = exclusion)
-
-chemicalSummary <- chemicalSummary %>%
- left_join(select(endPointInfo,
- endPoint=assay_component_endpoint_name,
- subFamily=intended_target_family_sub,
- gene_symbol=intended_target_gene_symbol), by="endPoint") %>%
- left_join(select(chem_info, CAS, `Chemical Name`), by="CAS")
-
-tableData <- chemicalSummary %>%
- rename(Chemical=`Chemical Name`) %>%
- group_by(site, endPoint, Family=Bio_category, subFamily, gene_symbol, date, Chemical) %>%
- summarize(sumEAR = sum(EAR)) %>%
- group_by(site, endPoint, Family, subFamily, gene_symbol, Chemical) %>%
- summarize(maxEAR = max(sumEAR)) %>%
- group_by(endPoint, Family, subFamily, gene_symbol, Chemical) %>%
- summarize(nSites = sum(maxEAR > 10^-3)) %>%
- data.frame() %>%
- filter(nSites > 0) %>%
- spread(Chemical, nSites) %>%
- arrange(Family, subFamily, gene_symbol) %>%
- select(endPoint, Family, subFamily, gene_symbol, everything())
-
-tableData2 <- select(tableData, -endPoint, -Family, -subFamily, -gene_symbol)
-tableData$nChems <- apply(tableData2, MARGIN = 1, function(x) sum(x>0, na.rm = TRUE))
-
-# tableData <- tableData %>%
-# left_join(AOP,by="gene_symbol")
-
-orderedCols <- data.frame(chnm = levels(chemicalSummary$chnm),
- stringsAsFactors = FALSE) %>%
- left_join(distinct(select(chemicalSummary, chnm, `Chemical Name`)), by = "chnm")
-
-orderedCols <- orderedCols$`Chemical Name`[which(orderedCols$`Chemical Name` %in% names(tableData))]
-
-tableData <- tableData[,c("Family", "subFamily","gene_symbol",
- "endPoint", "nChems",rev(orderedCols))]
-
-
-datatable(tableData,
- rownames = FALSE,
- extensions = 'Buttons',
- options = list(
- dom = 'Bfrtip',
- buttons = list('colvis', list(
- extend = 'collection',
- buttons = list(list(extend='csv',
- filename = 'siteTable'),
- list(extend='excel',
- filename = 'siteTable'),
- list(extend='pdf',
- filename= 'fullTable')),
- text = 'Download')
- )))# write.csv(tableData, file="wholeEnchilada.csv", row.names = FALSE, na = "")Exposure activity ratios (EARs) using ToxCast endpoints for screening of potential adverse biological impact in sample analysis results from 57 tributaries of the Great Lakes, 2010-2013 (A and B), toxicity quotients comparing established water quality criteria to sample results (C), and 17-beta estradiol equivalency quotients (ng E2/L) for compounds with established estradiol equivalency factors (D). Compounds are grouped by chemical class and ordered by largest to smallest median EAR. Chemicals where water quality guidelines could not be identified are indicated with “*”. E2, 17-β estradiol; n, number of sampling locations with detections of each chemical. [Boxes, 25th to 75th percentiles; dark line, median; whiskers, 1.5 x the interquartile range (IQR); circles, values outside 1.5 x the IQR. n, number of sites with detections of chemicals that have activity for endpoints in designated intended target families.]
-library(readxl)
-library(toxEval)
-library(dplyr)
-library(tidyr)
-library(ggplot2)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-
-full_path <- file.path(path_to_tox, file_name)
-
-chem_data <- read_excel(full_path, sheet = "Data")
-chem_info <- read_excel(full_path, sheet = "Chemicals")
-chem_site <- read_excel(full_path, sheet = "Sites")
-exclusion <- read_excel(full_path, sheet = "Exclude")
-
-#Trim names for graph:
-
-chem_info$Class[chem_info$Class == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-chem_info$Class[chem_info$Class == "Detergent Metabolites"] <- "Detergent"
-chem_info$Class[chem_info$Class == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-ACClong <- get_ACC(chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list = NULL,
- ACClong,
- filtered_ep,
- chem.data = chem_data,
- chem.site = chem_site,
- chem.info = chem_info,
- exclusion = exclusion)
-
-
-# Make a "summary" of EAR's using Water Quality Guidelines instead of toxCast:
-guideline_sum <- chem_info %>%
- gather(endPoint, WQ_value, -CAS) %>%
- filter(WQ_value != "-") %>%
- mutate(WQ_value = as.numeric(WQ_value)) %>%
- right_join(chem_data, by="CAS")
-
-WQ <- guideline_sum %>%
- filter(endPoint %in% c("AqT_EPA_acute","AqT_EPA_chronic",
- "AqT_other_chronic","AqT_other_acute")) %>%
- mutate(EAR = Value/WQ_value) %>%
- group_by(SiteID,`Sample Date`,CAS) %>%
- summarise(sumEAR=sum(EAR)) %>%
- data.frame() %>%
- group_by(SiteID, CAS) %>%
- summarise(maxEAR=max(sumEAR)) %>%
- data.frame() %>%
- mutate(guide_up = "Water Quality Guideline") %>%
- mutate(guide_side = "Traditional")
-
-EEQ <- guideline_sum %>%
- filter(endPoint %in% c("EEF_max_in.vitro_or_in.vivo")) %>%
- mutate(EAR = Value*WQ_value*1000) %>%
- group_by(SiteID,`Sample Date`,CAS) %>%
- summarise(sumEAR=sum(EAR)) %>%
- data.frame() %>%
- group_by(SiteID, CAS) %>%
- summarise(maxEAR=max(sumEAR)) %>%
- data.frame() %>%
- mutate(guide_up = "EEQ") %>%
- mutate(guide_side = "Traditional")
-
-# Make a "summary" of EAR's using toxCast:
-
-toxCast <- chemicalSummary %>%
- select(-Bio_category, -shortName, -chnm) %>%
- group_by(site,date,CAS, Class) %>%
- summarise(sumEAR=sum(EAR)) %>%
- data.frame() %>%
- group_by(site, CAS, Class) %>%
- summarise(maxEAR=max(sumEAR)) %>%
- data.frame() %>%
- rename(SiteID = site) %>%
- mutate(guide_side = "ToxCast") %>%
- left_join(select(chem_info, CAS, `Chemical Name`), by="CAS")
-
-# We need extra rows for comparing tox with WQG and EEQ:
-tox_WQ <- toxCast %>%
- mutate(guide_up = "Water Quality Guideline")
-
-tox_EEQ <- filter(toxCast, CAS %in% unique(EEQ$CAS)) %>%
- mutate(guide_up = "EEQ")
-
-cbValues <- c("#DCDA4B","#999999","#00FFFF","#CEA226","#CC79A7","#4E26CE",
- "#FFFF00","#78C15A","#79AEAE","#FF0000","#00FF00","#B1611D",
- "#FFA500","#F4426e")
-
-order_Class <- toxCast %>%
- group_by(Class,`Chemical Name`) %>%
- summarise(median = median(maxEAR[maxEAR != 0])) %>%
- data.frame() %>%
- arrange(desc(median)) %>%
- filter(!duplicated(Class)) %>%
- arrange(median)
-
-EEQ <- EEQ %>%
- left_join(select(chem_info, CAS, `Chemical Name`, Class), by="CAS")
-WQ <- WQ %>%
- left_join(select(chem_info, CAS, `Chemical Name`, Class), by="CAS")
-
-
-graphData <- bind_rows(tox_WQ, tox_EEQ, EEQ, WQ)
-
-#Primary ordering needs to be the highest -> lowest class, then order by median
-orderChem <- bind_rows(tox_WQ,
- filter(EEQ, !(CAS %in% unique(tox_WQ$CAS))),
- filter(WQ, !(CAS %in% unique(tox_WQ$CAS)))) %>%
- group_by(`Chemical Name`,Class) %>%
- summarise(median = quantile(maxEAR[maxEAR != 0],0.5)) %>%
- data.frame() %>%
- mutate(Class = factor(Class, levels=order_Class$Class)) %>%
- arrange(Class, !is.na(median), median)
-
-orderedLevels <- as.character(orderChem$Chemical.Name)
-
-
-graphData <-graphData %>%
- mutate(Class = factor(Class, levels=rev(order_Class$Class)),
- `Chemical Name` = factor(`Chemical Name`, levels=orderedLevels),
- guide_side = factor(guide_side),
- guide_up = factor(guide_up, levels = c("Water Quality Guideline","EEQ")))
-
-levels(graphData$guide_side) <- c("ToxCast\nMaximum EAR Per Site",
- "Traditional\nMaximum Quotient Per Site")
-
-#Adding counts to the side:
-countNonZero <- graphData %>%
- select(SiteID, `Chemical Name`,guide_side,guide_up, maxEAR) %>%
- group_by(SiteID, `Chemical Name`,guide_side,guide_up) %>%
- summarise(meanEAR = mean(maxEAR, na.rm=TRUE)) %>%
- group_by(`Chemical Name`,guide_side,guide_up) %>%
- summarise(nonZero = as.character(sum(meanEAR>0))) %>%
- data.frame() %>%
- select(Chemical.Name, guide_up, nonZero) %>%
- distinct() %>%
- mutate(guide_side = factor("ToxCast\nMaximum EAR Per Site",
- levels = levels(graphData$guide_side)),
- guide_up = factor(guide_up, levels = levels(graphData$guide_up)),
- `Chemical Name` = factor(Chemical.Name,
- levels = levels(graphData$`Chemical Name`)))
-
-# WQ: Astricts to chemicals with no endpoints:
-astrictData_WQ <- countNonZero %>%
- mutate(guide_side = factor("Traditional\nMaximum Quotient Per Site",
- levels = levels(graphData$guide_side))) %>%
- filter(guide_up == "Water Quality Guideline") %>%
- mutate(nonZero = "*") %>%
- filter(!(`Chemical Name` %in% unique(WQ$`Chemical Name`)))
-
-# EEQ: Astricts to chemicals with no endpoints:
-astrictData_EEQ <- countNonZero %>%
- mutate(guide_side = factor("ToxCast\nMaximum EAR Per Site",
- levels = levels(graphData$guide_side))) %>%
- filter(guide_up == "EEQ") %>%
- mutate(nonZero = "*") %>%
- filter(!(`Chemical Name` %in% unique(tox_EEQ$`Chemical Name`)))
-
-# Label upper right corner for each facet (probably an easier way...):
-textData <- select(graphData, guide_up, guide_side) %>%
- distinct() %>%
- mutate(textExplain = c("A","B","C","D"),
- y = c(10,10,100,100),
- `Chemical Name` = factor(rep("4-Nonylphenol (sum of all isomers)",4), levels = levels(graphData$`Chemical Name`)))
-
-toxPlot_All <- ggplot(data=graphData) +
- scale_y_log10(labels=fancyNumbers) +
- geom_boxplot(aes(x=`Chemical Name`, y=maxEAR, fill=Class),
- lwd=0.1,outlier.size=1) +
- facet_grid(guide_up ~ guide_side, scales = "free", space = "free") +
- theme_bw() +
- scale_x_discrete(drop=TRUE) +
- coord_flip() +
- theme(axis.text = element_text( color = "black"),
- axis.text.y = element_text(size=7),
- axis.title=element_blank(),
- panel.background = element_blank(),
- plot.background = element_rect(fill = "transparent",colour = NA),
- strip.background = element_rect(fill = "transparent",colour = NA),
- strip.text.y = element_blank()) +
- guides(fill=guide_legend(ncol=6)) +
- theme(legend.position="bottom",
- legend.justification = "left",
- legend.background = element_rect(fill = "transparent", colour = "transparent"),
- legend.title=element_blank(),
- legend.text = element_text(size=8),
- legend.key.height = unit(1,"line")) +
- scale_fill_manual(values = cbValues, drop=FALSE)
-
-ymin <- 10^-6
-ymax <- ggplot_build(toxPlot_All)$layout$panel_ranges[[1]]$y.range[2]
-
-toxPlot_All_withLabels <- toxPlot_All +
- geom_text(data=countNonZero, aes(x= `Chemical Name`, label = nonZero, y=ymin), size=2.5) +
- geom_text(data = textData, aes(x=`Chemical Name`, label=textExplain, y=y),
- size = 3) +
- geom_text(data = astrictData_WQ, aes(x=`Chemical Name`, label=nonZero, y=10^-5),
- size=5, vjust = 0.70) +
- geom_text(data = astrictData_EEQ, aes(x=`Chemical Name`, label=nonZero, y=3.3*ymin),
- size=5, vjust = 0.70)
-
-toxPlot_All_withLabels
Number of individual chemicals with at least one sample that resulted in an exposure activity ratio > 10-3 for each site.
-library(toxEval)
-library(dplyr)
-library(ggplot2)
-library(grid)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-#Ordering the sites to flow "downstream" of the Great Lakes:
-sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal",
- "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns",
- "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI",
- "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc",
- "Manistee","Fox","Oconto","Peshtigo","Menominee",
- "Indian","Cheboygan","Ford","Escanaba","Manistique",
- "ThunderBay","AuSable","Rifle","Saginaw","BlackMI",
- "Clinton","Rouge","HuronMI","Raisin","Maumee",
- "Portage","Sandusky","HuronOH","Vermilion","BlackOH",
- "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda",
- "Genesee","Oswego","BlackNY","Oswegatchie","Grass",
- "Raquette","StRegis")
-
-
-threshold <- 10^-3
-
-graphData <- chemicalSummary %>%
- group_by(site, date, chnm) %>%
- summarize(sumEAR = sum(EAR)) %>%
- group_by(site, chnm) %>%
- summarize(maxEAR = max(sumEAR),
- count = n()) %>%
- group_by(site, count) %>%
- summarize(nChem = sum(maxEAR > threshold)) %>%
- data.frame() %>%
- left_join(select(tox_list[["chem_site"]], site=SiteID, `Short Name`, site_grouping),
- by = "site") %>%
- mutate(`Short Name` = factor(`Short Name`, levels = sitesOrdered)) %>%
- mutate(site_grouping = factor(site_grouping, levels = c("Lake Superior",
- "Lake Michigan",
- "Lake Huron",
- "Lake Erie",
- "Lake Ontario")))
-
-countPlot <- ggplot(graphData, aes(x=`Short Name`))+
- geom_bar(aes(y=nChem),
- stat = "identity",
- fill = "steelblue") +
- geom_text(aes(y=-1, label = count), size = 2.5) +
- theme_bw() +
- facet_grid(. ~ site_grouping, scales="free", space="free") +
- xlab("") +
- ylab("Number of Chemicals\n with EARmax > 0.001") +
- theme(axis.text.x = element_text( angle = 90,vjust=0.5,hjust = 1)) +
- theme(strip.text.y = element_text(angle=0, hjust=0, size=7),
- strip.text.x = element_text(size = 8),
- strip.background = element_rect(fill="transparent", colour = NA),
- axis.text = element_text(size=8),
- panel.spacing = unit(0.05, "lines"),
- panel.grid.major = element_blank(),
- panel.grid.minor = element_blank(),
- plot.background = element_rect(fill = "transparent",colour = NA))
-
-countPlot
-grid.text("# Samples:",
- x = unit(.04, "npc"),
- y = unit(.19, "npc"), gp=gpar(fontsize=7))
Boxplots of the maximum summation of exposure activity ratios for each site by individual intended target families computed using individual water samples collected from 57 Great Lakes Tributaries from 2010-2013. Boxplots include only those sites that had detections for chemicals that influence the intended target families in ToxCast assays. Chemicals that influence the intended target family transferase were not detected. None of the monitored chemicals influence endpoints from the intended target families phosphatase, esterase, hydrolase, lyase, methyltransferase, and miscellaneous protein (all not included in this figure). [Boxes, 25th to 75th percentiles; dark line, median; whiskers, 1.5 x the interquartile range (IQR); circles, values outside 1.5 x the IQR. n, number of sites with detections of chemicals that have activity for endpoints in designated intended target families.]
-library(toxEval)
-library(grid)
-
-path_to_tox <- system.file("extdata", package="toxEval")
-file_name <- "OWC_data_fromSup.xlsx"
-full_path <- file.path(path_to_tox, file_name)
-
-tox_list <- create_toxEval(full_path)
-
-ACClong <- get_ACC(tox_list$chem_info$CAS)
-ACClong <- remove_flags(ACClong)
-
-cleaned_ep <- clean_endPoint_info(endPointInfo)
-filtered_ep <- filter_groups(cleaned_ep)
-
-chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep)
-
-#Trim some names:
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent"
-levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance"
-
-bioPlot <- plot_tox_boxplots(chemicalSummary,
- category = "Biological",
- manual_remove = c("Transferase","Undefined"))
-
-bioPlot
-grid.text("# Samples",
- x = unit(.22, "npc"),
- y = unit(.995, "npc"), gp=gpar(fontsize=7))
Could also do chemical classes:
-classPlot <- plot_tox_boxplots(chemicalSummary,
- category = "Chemical Class")
-classPlot
-grid.text("# Samples",
- x = unit(.22, "npc"),
- y = unit(.995, "npc"), gp=gpar(fontsize=7))
Could also or chemicals:
-chemPlot <- plot_tox_boxplots(chemicalSummary,
- category = "Chemical")
-chemPlot
-grid.text("# Samples",
- x = unit(.35, "npc"),
- y = unit(.995, "npc"), gp=gpar(fontsize=7))
-* Class: A character column defining the class of chemicals. Most `toxEval` functions will allow groupings by either chemical, class (as defined here), or biological grouping (will be discussed in section TODO:XXXXXXXX)
+The sidebar offers a host of options for customizing the analysis. This document will attempt to describe the logistics of options. This document will NOT go into the scientific details of what options to choose. That will need to be up to the user.
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
-## Sites
+### A. Load Excel File
-The "Sites" tab needs to have at a minimum 4 columns: "SiteID", "Short Name", "dec_lon", and "dec_lat". They can be in any order, but the first row of sheet should be the header (column names).
+Once the Shiny App has been started, the first step is to load the data. The data should be prepared as described in the "Prepare Data" vignette. Click on the "Browse" button and navigate to the properly prepared Excel file. Once a file is picked, the data will be loaded into the application. There is a progress bar while the data loads, but sometimes it may take a little more time to view the data as each individual graph/map/table also takes some time to compute.
-* SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique "SiteID" values *must* match the "SiteID" values in the "Data" sheet. There is no requirements on formatting for this column.
+### B. 'Group By' option
-* Short Name: This character column is the name associated with the SiteID that will be used in the graphs and tables. The names should be sufficiently descriptive, but as short as possible to optimize the area on the graph devoted to the data and not the labels.
+The "Group By" radio buttons affect all output in the application. For each function, we will be summarizing the data by summing individual EARs. How we group those summations depends on this choice.
-* dec_lon: This numeric column is the decimal longitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most `toxEval` functions will not require this column.
+The "Group" choice will group all of the endPoints that have similar groupings based on the options as determined by the designated "Annotation". The default annotation is "intended_target_family". With that choice, the "Group" radio button groups all EARs by the unique categories in the "intended_target_family" category.
-* dec_lat: This numeric column is the decimal latitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most `toxEval` functions will not require this column.
+The "Chemical" choice will group all of the endPoints in a single chemical (as defined by unique CAS).
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
+The "Class" choice will group all of the endpoints in all of the chemicals as defined in the "Chemical" tab in the Excel file.
-## Exclude
+### C. Mean and Max: Summarize by site
-The "Exclude" tab is an optional tab. If used, the "Exclude" tab needs to have at a minimum 2 columns: "CAS" and "endPoint". They can be in any order, but the first row of sheet should be the header (column names).
+The options are "MeanEAR" and "MaxEAR", and they dictate how to summarize the EARs per site. Each site may have different numbers of collected samples, and depending on the scope of your study, it may be more important to analyze the mean of the site, or look at the maximum at each site.
-* endPoint: A character column define a specific ToxCast endpoint to ignore. IF the "CAS" in the corresponding row is empty, the endPoint will be completely excluded from the `toxEval` analysis. For example, maybe it was decided that TOX21_p53_BLA_p3_ratio was not an appropriate endPoint to consider in the analysis. We can exclude all TOX21_p53_BLA_p3_ratio endPoints by adding it to the Exclude tab. IF the "CAS" in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
+### D. Download benchmarks
-* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. IF the "endPoint" in the corresponding row is empty, the chemical will be completely excluded from the `toxEval` analysis. For example, maybe it was discovered that DEET was contaminated in the sampling method. We can exclude all DEET measurements by adding 134-62-3 to the Exclude tab. IF the "endPoint" in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
+Clicking on this button will download the a comma delimited (csv) file that could be used in the "Benchmark" tab (as described in the "Prepare Data" vignette). The provided columns are "CAS", "Chemical" (name), "flags", "endPoint", "ACC", "MlWt", "Value", "groupCol", and "assaysFull".
-There could be a variety of reasons to exclude chemical/endpoint combinations. This will be discussed further in section TODO: XXXXXXXXX.
+The "ACC" column is in log(uM), and the "Value" column is the conversion from that log(uM) to ug/L.
+
+### E. Assay
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
+
-## Benchmarks
+The assays dropdown menu offers the option to remove certain assays from the analysis. By default, the "BioSeek" assay is not included. Clicking on any of the options will not have an immediate affect until the "Switch Assays" button is clicked.
-The "Benchmarks" tab is an optional tab. The benchmark tab can be used to over-ride the ToxCast endpoints. For example, there may be a need to perform similar `ToxEval` analysis using EPA benchmarks instead of ToxCast endpoints.
+### F. Annotations
-For the functions to work seamlessly with all `toxEval` functions, the "Benchmarks" tab needs to have at a minimum 5 columns: "CAS", "Chemical", "endPoint", "Value", and "groupCol". They can be in any order, but the first row of sheet should be the header (column names).
+The "Annotations" dropdown offers the choice of which ToxCast grouping to analyze. By default, the "intended_target_family" is picked, but there may be other more appropriate options based on the scope of the analysis. Choosing a new annotation will not have an immediate affect until the "Switch Annotation" button is clicked.
-* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry.
+### G. Group
-* Chemical: A character column defining the name of the chemicals.
+The options in the "Group" dropdown change depending on the choice in Annotations. For "intended_target_family" for example, there would be the choices such as "Nuclear Receptors", "Cell Cycle", "DNA Binding", etc. You can ignore certain groups ("Background Measurement" for instance) by leaving the options unchecked. Clicking on and off the checkboxes will not have an immediate affect until the "Switch Groups" button is clicked.
-* endPoint: A character column naming the benchmark
+### H. Flags
-* Value: The concentration (in identical units as what is reported in the "Data" tab) of the benchmark.
+
-* groupCol: A character column that groups endpoints. This is analogous to the Biological groupings from the ToxCast analysis.
+The choices in the "Flags" dropdown are which flags to include in your analysis. By default, endPoints flagged with "Noisy Data", "Only one conc above baseline, active", and "Hit-call potentially confounded by overfitting" are included in the analysis. The scope of the analysis may cause you to want to include more or less flagged endPoints. Clicking on the flag checkboxes will not have an immediate affect until the "Switch Flags" button is clicked.
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
+### I. Sites
-# Checking the data
+By default, the analysis is done on all the sites. However, you can dig down to an individual sites using this dropdown menu. Each graph and table may show the individual site data a bit different than the full data set analysis, and that is described in the "Basic Workflow" vignette
-Loading the Excel
+### J. Hit Threshold
-## Alternative method
+There are a few outputs (from the tabs: Hit Count, Site Hits, and Endpoint Hits) that will calculate the number of "Hits"...which is simply the number of times the EAR is above a designated threshold. That threshold value is defined here. Changing the threshold will not have an immedate affect until the "Change Hit Threshold" button is clicked.
+## Main Output
-# Using the Shiny Application
+Each plot and table offered in the app is described in the "Basic Workflow" vignette. Please refer there for more information.
-## Loading data
+For each plot, there is an option to download the plot or data that is in the plot. The R-code to generate the output is also provided under each output. This would be the ideal way to customize any additional output.
-## Sidebar
-
-# Getting Started
+# Package logistics
## Installation of R and RStudio
@@ -163,9 +154,10 @@ install.packages("toxEval")
## Update toxEval
+
+
Regularly, it is a good idea to update *ALL* your packages in R. If using RStudio, this is quite easy, there's an Update button in the "Packages" tab. This checks CRAN and GRAN for updates. It is a good idea to click this update regularly.
-
Within the R-console, packages can be update with the command:
```{r eval=FALSE}
diff --git a/inst/doc/shinyApp.html b/inst/doc/shinyApp.html
index 9d585b3b..8f069737 100644
--- a/inst/doc/shinyApp.html
+++ b/inst/doc/shinyApp.html
@@ -12,7 +12,7 @@
-The toxEval R-package includes a set of functions to analyze, visualize, and organize measured concentration data as it relates to ToxCast data. These functions allow great flexibly for exploring the potential biological affects of measured chemicals. Also included in the package is a browser-based application made from the shiny R-package. There is less customization from the application, but it is a convenient way to explore the data.
This vignette describes the process to prepare the data and use the shiny application. There is also a section on installing and updating the package, since it is not on CRAN.
-The data should be prepared in a Microsoft Excel file. There are 3 mandatory sheets (also known as tabs), and 2 optional sheets. The sheets should appear like this (although the order is not important):
-
-Organize Sheets
-Each sheet has mandatory columns; the order of the columns is not important, but the names of the columns is important. Additional columns can be included but will be ignored. The top row of each sheet should contain the column names (headers), no rows should be initially skipped, and no titles or comment rows should precede the data.
-The “Data” tab needs to have at a minimum 4 columns: “CAS”, “SiteID”, “Value”, and “Sample Date”. They can be in any order, but the first row of sheet should be the header (column names).
-CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose “Format Cells”, and choose “Text” as the category to assure they retain their format. The unique CAS values in this column must match with the CAS values in the “Chemicals” sheet.
SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique “SiteID” values must match the “SiteID” values in the “Sites” sheet. There is no requirements on formatting for this column.
Value: This column is numeric. It should be the concentration of the chemical in \(\mu\)g/L.
Sample Date: Currently this column only serves to identify unique samples. Dates and times as formatted in Excel, as well as integer values are acceptable. The actual date and times are not currently used to perform any time series analysis.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
As an example, the first several rows of a minimal example would look like this:
-
-Example Data
-The “Chemicals” tab needs to have at a minimum 2 columns: “CAS” and “Class”. They can be in any order, but the first row of sheet should be the header (column names).
-CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose “Format Cells”, and choose “Text” as the category to assure they retain their format. The unique CAS values in this column must match with the CAS values in the “Data” sheet.
Class: A character column defining the class of chemicals. Most toxEval functions will allow groupings by either chemical, class (as defined here), or biological grouping (will be discussed in section TODO:XXXXXXXX)
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
The “Sites” tab needs to have at a minimum 4 columns: “SiteID”, “Short Name”, “dec_lon”, and “dec_lat”. They can be in any order, but the first row of sheet should be the header (column names).
-SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique “SiteID” values must match the “SiteID” values in the “Data” sheet. There is no requirements on formatting for this column.
Short Name: This character column is the name associated with the SiteID that will be used in the graphs and tables. The names should be sufficiently descriptive, but as short as possible to optimize the area on the graph devoted to the data and not the labels.
dec_lon: This numeric column is the decimal longitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most toxEval functions will not require this column.
dec_lat: This numeric column is the decimal latitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most toxEval functions will not require this column.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
The “Exclude” tab is an optional tab. If used, the “Exclude” tab needs to have at a minimum 2 columns: “CAS” and “endPoint”. They can be in any order, but the first row of sheet should be the header (column names).
-endPoint: A character column define a specific ToxCast endpoint to ignore. IF the “CAS” in the corresponding row is empty, the endPoint will be completely excluded from the toxEval analysis. For example, maybe it was decided that TOX21_p53_BLA_p3_ratio was not an appropriate endPoint to consider in the analysis. We can exclude all TOX21_p53_BLA_p3_ratio endPoints by adding it to the Exclude tab. IF the “CAS” in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. IF the “endPoint” in the corresponding row is empty, the chemical will be completely excluded from the toxEval analysis. For example, maybe it was discovered that DEET was contaminated in the sampling method. We can exclude all DEET measurements by adding 134-62-3 to the Exclude tab. IF the “endPoint” in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
There could be a variety of reasons to exclude chemical/endpoint combinations. This will be discussed further in section TODO: XXXXXXXXX.
-Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
The “Benchmarks” tab is an optional tab. The benchmark tab can be used to over-ride the ToxCast endpoints. For example, there may be a need to perform similar ToxEval analysis using EPA benchmarks instead of ToxCast endpoints.
For the functions to work seamlessly with all toxEval functions, the “Benchmarks” tab needs to have at a minimum 5 columns: “CAS”, “Chemical”, “endPoint”, “Value”, and “groupCol”. They can be in any order, but the first row of sheet should be the header (column names).
CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry.
Chemical: A character column defining the name of the chemicals.
endPoint: A character column naming the benchmark
Value: The concentration (in identical units as what is reported in the “Data” tab) of the benchmark.
groupCol: A character column that groups endpoints. This is analogous to the Biological groupings from the ToxCast analysis.
Note: Additional columns may be useful to organize the data. They will not hinder a toxEval analysis.
Loading the Excel
-All of the output on the Shiny app can be created directly in R as well. Using the app is a quick and convenient way to explore your data. For more customization, the R-code to produce each graph and table is displayed in the app. That is a good starting-point for a custom analysis. See the help file for any functions on details and example workflows.
+To run the app, load the toxEval package, and run the explore_endpoints function:
library(toxEval)
+explore_endpoints()Your default browser will open up and display the following page:
+
+Main Layout of Shiny App
+Generally the app can be divided into 3 main sections:
+The left sidebar (with the black background) will be used to load the data and set up the conditions for analysis. The options here will be described in the next section.
+The top main section includes a variety of tabs to choose from to explore the data in different ways. Each section will be described in the section “Main Output”.
+The bottom section “R Code:” shows the exact code you could use to run directly in R to reproduce the analysis. The code can be copied and pasted directly into the R console, or (preferred for reproducible!) saved in an R script. One important note is that the local path to the file is not recorded, so if your data file is not located in your current working directory, you’ll need to add the path to your file name.
+
The sidebar offers a host of options for customizing the analysis. This document will attempt to describe the logistics of options. This document will NOT go into the scientific details of what options to choose. That will need to be up to the user.
+Once the Shiny App has been started, the first step is to load the data. The data should be prepared as described in the “Prepare Data” vignette. Click on the “Browse” button and navigate to the properly prepared Excel file. Once a file is picked, the data will be loaded into the application. There is a progress bar while the data loads, but sometimes it may take a little more time to view the data as each individual graph/map/table also takes some time to compute.
+The “Group By” radio buttons affect all output in the application. For each function, we will be summarizing the data by summing individual EARs. How we group those summations depends on this choice.
+The “Group” choice will group all of the endPoints that have similar groupings based on the options as determined by the designated “Annotation”. The default annotation is “intended_target_family”. With that choice, the “Group” radio button groups all EARs by the unique categories in the “intended_target_family” category.
+The “Chemical” choice will group all of the endPoints in a single chemical (as defined by unique CAS).
+The “Class” choice will group all of the endpoints in all of the chemicals as defined in the “Chemical” tab in the Excel file.
+The options are “MeanEAR” and “MaxEAR”, and they dictate how to summarize the EARs per site. Each site may have different numbers of collected samples, and depending on the scope of your study, it may be more important to analyze the mean of the site, or look at the maximum at each site.
+Clicking on this button will download the a comma delimited (csv) file that could be used in the “Benchmark” tab (as described in the “Prepare Data” vignette). The provided columns are “CAS”, “Chemical” (name), “flags”, “endPoint”, “ACC”, “MlWt”, “Value”, “groupCol”, and “assaysFull”.
+The “ACC” column is in log(uM), and the “Value” column is the conversion from that log(uM) to ug/L.
+
The assays dropdown menu offers the option to remove certain assays from the analysis. By default, the “BioSeek” assay is not included. Clicking on any of the options will not have an immediate affect until the “Switch Assays” button is clicked.
+The “Annotations” dropdown offers the choice of which ToxCast grouping to analyze. By default, the “intended_target_family” is picked, but there may be other more appropriate options based on the scope of the analysis. Choosing a new annotation will not have an immediate affect until the “Switch Annotation” button is clicked.
+The options in the “Group” dropdown change depending on the choice in Annotations. For “intended_target_family” for example, there would be the choices such as “Nuclear Receptors”, “Cell Cycle”, “DNA Binding”, etc. You can ignore certain groups (“Background Measurement” for instance) by leaving the options unchecked. Clicking on and off the checkboxes will not have an immediate affect until the “Switch Groups” button is clicked.
+
The choices in the “Flags” dropdown are which flags to include in your analysis. By default, endPoints flagged with “Noisy Data”, “Only one conc above baseline, active”, and “Hit-call potentially confounded by overfitting” are included in the analysis. The scope of the analysis may cause you to want to include more or less flagged endPoints. Clicking on the flag checkboxes will not have an immediate affect until the “Switch Flags” button is clicked.
+By default, the analysis is done on all the sites. However, you can dig down to an individual sites using this dropdown menu. Each graph and table may show the individual site data a bit different than the full data set analysis, and that is described in the “Basic Workflow” vignette
+There are a few outputs (from the tabs: Hit Count, Site Hits, and Endpoint Hits) that will calculate the number of “Hits”…which is simply the number of times the EAR is above a designated threshold. That threshold value is defined here. Changing the threshold will not have an immedate affect until the “Change Hit Threshold” button is clicked.
+Each plot and table offered in the app is described in the “Basic Workflow” vignette. Please refer there for more information.
+For each plot, there is an option to download the plot or data that is in the plot. The R-code to generate the output is also provided under each output. This would be the ideal way to customize any additional output.
+This section should only need to be done once per computer. The following link walks you through an installation of R and RStudio:
If you follow those instructions exactly, you should have the USGS R repository (GRAN) added to your R profile. If that step doesn’t ring a bell, paste the following into your R console:
@@ -208,32 +195,29 @@This section should also only have to be done once. It assumes the USGS R repository (GRAN) was added to your R profile as described above.
install.packages("toxEval")
Regularly, it is a good idea to update ALL your packages in R. If using RStudio, this is quite easy, there’s an Update button in the “Packages” tab. This checks CRAN and GRAN for updates. It is a good idea to click this update regularly.
-
-update
-Within the R-console, packages can be update with the command:
update.packages()Please consider reporting bugs and asking questions on the Issues page: https://github.com/USGS-R/toxEval/issues
This package has not been reviewed and approved as a USGS-product. The implication is that toxEval cannot be cited in a publication. Once the software has been reviewed and approved, basic citation information will be available using the function:
citation(package = "toxEval")This software is in the public domain because it contains materials that originally came from the U.S. Geological Survey (USGS), an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at https://www.usgs.gov/visual-id/credit_usgs.html#copyright
Although this software program has been used by the USGS, no warranty, expressed or implied, is made by the USGS or the U.S. Government as to the accuracy and functioning of the program and related program material nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by the USGS in connection therewith.
This software is provided “AS IS.”
diff --git a/inst/extdata/AOP_crosswalk.csv b/inst/extdata/AOP_crosswalk.csv deleted file mode 100644 index 0874197a..00000000 --- a/inst/extdata/AOP_crosswalk.csv +++ /dev/null @@ -1,2103 +0,0 @@ -Assay Endpoint ID,Component Endpoint Name,AOP #,AOP Title,KE#,Key Event Name,KeyEvent Type -1,ACEA_T47D_80hr_Negative,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1,ACEA_T47D_80hr_Negative,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1,ACEA_T47D_80hr_Negative,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1,ACEA_T47D_80hr_Negative,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1,ACEA_T47D_80hr_Negative,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1,ACEA_T47D_80hr_Negative,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1,ACEA_T47D_80hr_Negative,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1,ACEA_T47D_80hr_Negative,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1,ACEA_T47D_80hr_Negative,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1,ACEA_T47D_80hr_Negative,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1,ACEA_T47D_80hr_Negative,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1,ACEA_T47D_80hr_Negative,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1,ACEA_T47D_80hr_Negative,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -3,APR_HepG2_CellCycleArrest_1h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -3,APR_HepG2_CellCycleArrest_1h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -3,APR_HepG2_CellCycleArrest_1h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -3,APR_HepG2_CellCycleArrest_1h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -3,APR_HepG2_CellCycleArrest_1h_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -3,APR_HepG2_CellCycleArrest_1h_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -3,APR_HepG2_CellCycleArrest_1h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -3,APR_HepG2_CellCycleArrest_1h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -5,APR_HepG2_CellLoss_1h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -5,APR_HepG2_CellLoss_1h_dn,205,AoP from chemical insult to cell death,1263,Necrosis,AO -5,APR_HepG2_CellLoss_1h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -5,APR_HepG2_CellLoss_1h_dn,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -5,APR_HepG2_CellLoss_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -5,APR_HepG2_CellLoss_1h_dn,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -5,APR_HepG2_CellLoss_1h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -5,APR_HepG2_CellLoss_1h_dn,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -5,APR_HepG2_CellLoss_1h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -5,APR_HepG2_CellLoss_1h_dn,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -5,APR_HepG2_CellLoss_1h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -5,APR_HepG2_CellLoss_1h_dn,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -5,APR_HepG2_CellLoss_1h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -5,APR_HepG2_CellLoss_1h_dn,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -5,APR_HepG2_CellLoss_1h_dn,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -5,APR_HepG2_CellLoss_1h_dn,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -6,APR_HepG2_CellLoss_1h_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -6,APR_HepG2_CellLoss_1h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -6,APR_HepG2_CellLoss_1h_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -6,APR_HepG2_CellLoss_1h_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -6,APR_HepG2_CellLoss_1h_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -6,APR_HepG2_CellLoss_1h_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -6,APR_HepG2_CellLoss_1h_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -6,APR_HepG2_CellLoss_1h_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -9,APR_HepG2_MitoMass_1h_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -9,APR_HepG2_MitoMass_1h_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -9,APR_HepG2_MitoMass_1h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -9,APR_HepG2_MitoMass_1h_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -9,APR_HepG2_MitoMass_1h_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -9,APR_HepG2_MitoMass_1h_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -9,APR_HepG2_MitoMass_1h_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -9,APR_HepG2_MitoMass_1h_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -9,APR_HepG2_MitoMass_1h_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -9,APR_HepG2_MitoMass_1h_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -9,APR_HepG2_MitoMass_1h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -10,APR_HepG2_MitoMass_1h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -10,APR_HepG2_MitoMass_1h_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -10,APR_HepG2_MitoMass_1h_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -10,APR_HepG2_MitoMass_1h_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -10,APR_HepG2_MitoMass_1h_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -10,APR_HepG2_MitoMass_1h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -10,APR_HepG2_MitoMass_1h_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -10,APR_HepG2_MitoMass_1h_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -10,APR_HepG2_MitoMass_1h_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -10,APR_HepG2_MitoMass_1h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -10,APR_HepG2_MitoMass_1h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -10,APR_HepG2_MitoMass_1h_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -11,APR_HepG2_MitoMembPot_1h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -11,APR_HepG2_MitoMembPot_1h_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -11,APR_HepG2_MitoMembPot_1h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -11,APR_HepG2_MitoMembPot_1h_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -11,APR_HepG2_MitoMembPot_1h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -11,APR_HepG2_MitoMembPot_1h_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -11,APR_HepG2_MitoMembPot_1h_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -11,APR_HepG2_MitoMembPot_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -11,APR_HepG2_MitoMembPot_1h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -11,APR_HepG2_MitoMembPot_1h_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -12,APR_HepG2_MitoMembPot_1h_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -12,APR_HepG2_MitoMembPot_1h_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -12,APR_HepG2_MitoMembPot_1h_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -12,APR_HepG2_MitoMembPot_1h_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -12,APR_HepG2_MitoMembPot_1h_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -12,APR_HepG2_MitoMembPot_1h_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -12,APR_HepG2_MitoMembPot_1h_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -12,APR_HepG2_MitoMembPot_1h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -12,APR_HepG2_MitoMembPot_1h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -12,APR_HepG2_MitoMembPot_1h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -12,APR_HepG2_MitoMembPot_1h_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -13,APR_HepG2_MitoticArrest_1h_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -13,APR_HepG2_MitoticArrest_1h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -13,APR_HepG2_MitoticArrest_1h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -13,APR_HepG2_MitoticArrest_1h_dn,41,sustained AhR activation leading to rodent liver tumors,853,"Changes/Inhibition, Cellular Homeostasis and Apoptosis",KE -13,APR_HepG2_MitoticArrest_1h_dn,46,AFB1: Mutagenic Mode-of-action leading to hepatocellular Carcinoma (HCC),491,"Clonal Expansion/Cell Proliferation, to form Altered Hepatic Foci (AHF)",KE -13,APR_HepG2_MitoticArrest_1h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -13,APR_HepG2_MitoticArrest_1h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -13,APR_HepG2_MitoticArrest_1h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -13,APR_HepG2_MitoticArrest_1h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -13,APR_HepG2_MitoticArrest_1h_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -14,APR_HepG2_MitoticArrest_1h_up,46,AFB1: Mutagenic Mode-of-action leading to hepatocellular Carcinoma (HCC),491,"Clonal Expansion/Cell Proliferation, to form Altered Hepatic Foci (AHF)",KE -14,APR_HepG2_MitoticArrest_1h_up,41,sustained AhR activation leading to rodent liver tumors,853,"Changes/Inhibition, Cellular Homeostasis and Apoptosis",KE -17,APR_HepG2_OxidativeStress_1h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -17,APR_HepG2_OxidativeStress_1h_dn,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -17,APR_HepG2_OxidativeStress_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -17,APR_HepG2_OxidativeStress_1h_dn,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -17,APR_HepG2_OxidativeStress_1h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -17,APR_HepG2_OxidativeStress_1h_dn,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -17,APR_HepG2_OxidativeStress_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -17,APR_HepG2_OxidativeStress_1h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -17,APR_HepG2_OxidativeStress_1h_dn,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -17,APR_HepG2_OxidativeStress_1h_dn,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -17,APR_HepG2_OxidativeStress_1h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -17,APR_HepG2_OxidativeStress_1h_dn,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -17,APR_HepG2_OxidativeStress_1h_dn,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -17,APR_HepG2_OxidativeStress_1h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -18,APR_HepG2_OxidativeStress_1h_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -18,APR_HepG2_OxidativeStress_1h_up,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -18,APR_HepG2_OxidativeStress_1h_up,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -18,APR_HepG2_OxidativeStress_1h_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -18,APR_HepG2_OxidativeStress_1h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -18,APR_HepG2_OxidativeStress_1h_up,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -18,APR_HepG2_OxidativeStress_1h_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -18,APR_HepG2_OxidativeStress_1h_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -18,APR_HepG2_OxidativeStress_1h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -18,APR_HepG2_OxidativeStress_1h_up,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -18,APR_HepG2_OxidativeStress_1h_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -18,APR_HepG2_OxidativeStress_1h_up,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -18,APR_HepG2_OxidativeStress_1h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -18,APR_HepG2_OxidativeStress_1h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -21,APR_HepG2_StressKinase_1h_dn,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -21,APR_HepG2_StressKinase_1h_dn,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -21,APR_HepG2_StressKinase_1h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -21,APR_HepG2_StressKinase_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -21,APR_HepG2_StressKinase_1h_dn,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -21,APR_HepG2_StressKinase_1h_dn,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -21,APR_HepG2_StressKinase_1h_dn,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -21,APR_HepG2_StressKinase_1h_dn,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -21,APR_HepG2_StressKinase_1h_dn,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -21,APR_HepG2_StressKinase_1h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -21,APR_HepG2_StressKinase_1h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -21,APR_HepG2_StressKinase_1h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -21,APR_HepG2_StressKinase_1h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -21,APR_HepG2_StressKinase_1h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -22,APR_HepG2_StressKinase_1h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -22,APR_HepG2_StressKinase_1h_up,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -22,APR_HepG2_StressKinase_1h_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -22,APR_HepG2_StressKinase_1h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -22,APR_HepG2_StressKinase_1h_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -22,APR_HepG2_StressKinase_1h_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -22,APR_HepG2_StressKinase_1h_up,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -22,APR_HepG2_StressKinase_1h_up,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -22,APR_HepG2_StressKinase_1h_up,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -22,APR_HepG2_StressKinase_1h_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -22,APR_HepG2_StressKinase_1h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -22,APR_HepG2_StressKinase_1h_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -22,APR_HepG2_StressKinase_1h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -22,APR_HepG2_StressKinase_1h_up,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -23,APR_HepG2_CellCycleArrest_24h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -23,APR_HepG2_CellCycleArrest_24h_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -23,APR_HepG2_CellCycleArrest_24h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -23,APR_HepG2_CellCycleArrest_24h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -23,APR_HepG2_CellCycleArrest_24h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -23,APR_HepG2_CellCycleArrest_24h_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -23,APR_HepG2_CellCycleArrest_24h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -23,APR_HepG2_CellCycleArrest_24h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -25,APR_HepG2_CellLoss_24h_dn,205,AoP from chemical insult to cell death,1263,Necrosis,AO -25,APR_HepG2_CellLoss_24h_dn,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -25,APR_HepG2_CellLoss_24h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -25,APR_HepG2_CellLoss_24h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -25,APR_HepG2_CellLoss_24h_dn,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -25,APR_HepG2_CellLoss_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -25,APR_HepG2_CellLoss_24h_dn,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -25,APR_HepG2_CellLoss_24h_dn,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -25,APR_HepG2_CellLoss_24h_dn,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -25,APR_HepG2_CellLoss_24h_dn,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -25,APR_HepG2_CellLoss_24h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -25,APR_HepG2_CellLoss_24h_dn,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -25,APR_HepG2_CellLoss_24h_dn,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -26,APR_HepG2_CellLoss_24h_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -26,APR_HepG2_CellLoss_24h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -26,APR_HepG2_CellLoss_24h_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -26,APR_HepG2_CellLoss_24h_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -26,APR_HepG2_CellLoss_24h_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -26,APR_HepG2_CellLoss_24h_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -26,APR_HepG2_CellLoss_24h_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -26,APR_HepG2_CellLoss_24h_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -29,APR_HepG2_MitoMass_24h_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -29,APR_HepG2_MitoMass_24h_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -29,APR_HepG2_MitoMass_24h_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -29,APR_HepG2_MitoMass_24h_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -29,APR_HepG2_MitoMass_24h_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -29,APR_HepG2_MitoMass_24h_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -29,APR_HepG2_MitoMass_24h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -29,APR_HepG2_MitoMass_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -29,APR_HepG2_MitoMass_24h_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -29,APR_HepG2_MitoMass_24h_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -29,APR_HepG2_MitoMass_24h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -30,APR_HepG2_MitoMass_24h_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -30,APR_HepG2_MitoMass_24h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -30,APR_HepG2_MitoMass_24h_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -30,APR_HepG2_MitoMass_24h_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -30,APR_HepG2_MitoMass_24h_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -30,APR_HepG2_MitoMass_24h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -30,APR_HepG2_MitoMass_24h_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -30,APR_HepG2_MitoMass_24h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -30,APR_HepG2_MitoMass_24h_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -30,APR_HepG2_MitoMass_24h_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -30,APR_HepG2_MitoMass_24h_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -31,APR_HepG2_MitoMembPot_24h_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -31,APR_HepG2_MitoMembPot_24h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -31,APR_HepG2_MitoMembPot_24h_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -31,APR_HepG2_MitoMembPot_24h_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -31,APR_HepG2_MitoMembPot_24h_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -31,APR_HepG2_MitoMembPot_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -31,APR_HepG2_MitoMembPot_24h_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -31,APR_HepG2_MitoMembPot_24h_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -31,APR_HepG2_MitoMembPot_24h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -31,APR_HepG2_MitoMembPot_24h_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -31,APR_HepG2_MitoMembPot_24h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -32,APR_HepG2_MitoMembPot_24h_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -32,APR_HepG2_MitoMembPot_24h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -32,APR_HepG2_MitoMembPot_24h_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -32,APR_HepG2_MitoMembPot_24h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -32,APR_HepG2_MitoMembPot_24h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -32,APR_HepG2_MitoMembPot_24h_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -32,APR_HepG2_MitoMembPot_24h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -32,APR_HepG2_MitoMembPot_24h_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -32,APR_HepG2_MitoMembPot_24h_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -32,APR_HepG2_MitoMembPot_24h_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -32,APR_HepG2_MitoMembPot_24h_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -33,APR_HepG2_MitoticArrest_24h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -33,APR_HepG2_MitoticArrest_24h_dn,41,sustained AhR activation leading to rodent liver tumors,853,"Changes/Inhibition, Cellular Homeostasis and Apoptosis",KE -33,APR_HepG2_MitoticArrest_24h_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -33,APR_HepG2_MitoticArrest_24h_dn,46,AFB1: Mutagenic Mode-of-action leading to hepatocellular Carcinoma (HCC),491,"Clonal Expansion/Cell Proliferation, to form Altered Hepatic Foci (AHF)",KE -33,APR_HepG2_MitoticArrest_24h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -33,APR_HepG2_MitoticArrest_24h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -33,APR_HepG2_MitoticArrest_24h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -33,APR_HepG2_MitoticArrest_24h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -33,APR_HepG2_MitoticArrest_24h_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -33,APR_HepG2_MitoticArrest_24h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -34,APR_HepG2_MitoticArrest_24h_up,46,AFB1: Mutagenic Mode-of-action leading to hepatocellular Carcinoma (HCC),491,"Clonal Expansion/Cell Proliferation, to form Altered Hepatic Foci (AHF)",KE -34,APR_HepG2_MitoticArrest_24h_up,41,sustained AhR activation leading to rodent liver tumors,853,"Changes/Inhibition, Cellular Homeostasis and Apoptosis",KE -37,APR_HepG2_OxidativeStress_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -37,APR_HepG2_OxidativeStress_24h_dn,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -37,APR_HepG2_OxidativeStress_24h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -37,APR_HepG2_OxidativeStress_24h_dn,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -37,APR_HepG2_OxidativeStress_24h_dn,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -37,APR_HepG2_OxidativeStress_24h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -37,APR_HepG2_OxidativeStress_24h_dn,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -37,APR_HepG2_OxidativeStress_24h_dn,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -37,APR_HepG2_OxidativeStress_24h_dn,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -37,APR_HepG2_OxidativeStress_24h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -37,APR_HepG2_OxidativeStress_24h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -37,APR_HepG2_OxidativeStress_24h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -37,APR_HepG2_OxidativeStress_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -37,APR_HepG2_OxidativeStress_24h_dn,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -38,APR_HepG2_OxidativeStress_24h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -38,APR_HepG2_OxidativeStress_24h_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -38,APR_HepG2_OxidativeStress_24h_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -38,APR_HepG2_OxidativeStress_24h_up,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -38,APR_HepG2_OxidativeStress_24h_up,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -38,APR_HepG2_OxidativeStress_24h_up,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -38,APR_HepG2_OxidativeStress_24h_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -38,APR_HepG2_OxidativeStress_24h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -38,APR_HepG2_OxidativeStress_24h_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -38,APR_HepG2_OxidativeStress_24h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -38,APR_HepG2_OxidativeStress_24h_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -38,APR_HepG2_OxidativeStress_24h_up,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -38,APR_HepG2_OxidativeStress_24h_up,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -38,APR_HepG2_OxidativeStress_24h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -41,APR_HepG2_StressKinase_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -41,APR_HepG2_StressKinase_24h_dn,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -41,APR_HepG2_StressKinase_24h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -41,APR_HepG2_StressKinase_24h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -41,APR_HepG2_StressKinase_24h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -41,APR_HepG2_StressKinase_24h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -41,APR_HepG2_StressKinase_24h_dn,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -41,APR_HepG2_StressKinase_24h_dn,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -41,APR_HepG2_StressKinase_24h_dn,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -41,APR_HepG2_StressKinase_24h_dn,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -41,APR_HepG2_StressKinase_24h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -41,APR_HepG2_StressKinase_24h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -41,APR_HepG2_StressKinase_24h_dn,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -41,APR_HepG2_StressKinase_24h_dn,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -42,APR_HepG2_StressKinase_24h_up,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -42,APR_HepG2_StressKinase_24h_up,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -42,APR_HepG2_StressKinase_24h_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -42,APR_HepG2_StressKinase_24h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -42,APR_HepG2_StressKinase_24h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -42,APR_HepG2_StressKinase_24h_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -42,APR_HepG2_StressKinase_24h_up,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -42,APR_HepG2_StressKinase_24h_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -42,APR_HepG2_StressKinase_24h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -42,APR_HepG2_StressKinase_24h_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -42,APR_HepG2_StressKinase_24h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -42,APR_HepG2_StressKinase_24h_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -42,APR_HepG2_StressKinase_24h_up,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -42,APR_HepG2_StressKinase_24h_up,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -43,APR_HepG2_CellCycleArrest_72h_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -43,APR_HepG2_CellCycleArrest_72h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -43,APR_HepG2_CellCycleArrest_72h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -43,APR_HepG2_CellCycleArrest_72h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -43,APR_HepG2_CellCycleArrest_72h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -43,APR_HepG2_CellCycleArrest_72h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -43,APR_HepG2_CellCycleArrest_72h_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -43,APR_HepG2_CellCycleArrest_72h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -45,APR_HepG2_CellLoss_72h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -45,APR_HepG2_CellLoss_72h_dn,205,AoP from chemical insult to cell death,1263,Necrosis,AO -45,APR_HepG2_CellLoss_72h_dn,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -45,APR_HepG2_CellLoss_72h_dn,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -45,APR_HepG2_CellLoss_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -45,APR_HepG2_CellLoss_72h_dn,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -45,APR_HepG2_CellLoss_72h_dn,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -45,APR_HepG2_CellLoss_72h_dn,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -45,APR_HepG2_CellLoss_72h_dn,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -45,APR_HepG2_CellLoss_72h_dn,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -45,APR_HepG2_CellLoss_72h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -45,APR_HepG2_CellLoss_72h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -45,APR_HepG2_CellLoss_72h_dn,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -46,APR_HepG2_CellLoss_72h_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -46,APR_HepG2_CellLoss_72h_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -46,APR_HepG2_CellLoss_72h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -46,APR_HepG2_CellLoss_72h_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -46,APR_HepG2_CellLoss_72h_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -46,APR_HepG2_CellLoss_72h_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -46,APR_HepG2_CellLoss_72h_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -46,APR_HepG2_CellLoss_72h_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -49,APR_HepG2_MitoMass_72h_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -49,APR_HepG2_MitoMass_72h_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -49,APR_HepG2_MitoMass_72h_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -49,APR_HepG2_MitoMass_72h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -49,APR_HepG2_MitoMass_72h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -49,APR_HepG2_MitoMass_72h_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -49,APR_HepG2_MitoMass_72h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -49,APR_HepG2_MitoMass_72h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -49,APR_HepG2_MitoMass_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -49,APR_HepG2_MitoMass_72h_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -49,APR_HepG2_MitoMass_72h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -49,APR_HepG2_MitoMass_72h_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -50,APR_HepG2_MitoMass_72h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -50,APR_HepG2_MitoMass_72h_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -50,APR_HepG2_MitoMass_72h_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -50,APR_HepG2_MitoMass_72h_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -50,APR_HepG2_MitoMass_72h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -50,APR_HepG2_MitoMass_72h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -50,APR_HepG2_MitoMass_72h_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -50,APR_HepG2_MitoMass_72h_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -50,APR_HepG2_MitoMass_72h_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -50,APR_HepG2_MitoMass_72h_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -51,APR_HepG2_MitoMembPot_72h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -51,APR_HepG2_MitoMembPot_72h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -51,APR_HepG2_MitoMembPot_72h_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -51,APR_HepG2_MitoMembPot_72h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -51,APR_HepG2_MitoMembPot_72h_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -51,APR_HepG2_MitoMembPot_72h_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -51,APR_HepG2_MitoMembPot_72h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -51,APR_HepG2_MitoMembPot_72h_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -51,APR_HepG2_MitoMembPot_72h_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -51,APR_HepG2_MitoMembPot_72h_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -51,APR_HepG2_MitoMembPot_72h_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -51,APR_HepG2_MitoMembPot_72h_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -52,APR_HepG2_MitoMembPot_72h_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -52,APR_HepG2_MitoMembPot_72h_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -52,APR_HepG2_MitoMembPot_72h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -52,APR_HepG2_MitoMembPot_72h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -52,APR_HepG2_MitoMembPot_72h_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -52,APR_HepG2_MitoMembPot_72h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -52,APR_HepG2_MitoMembPot_72h_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -52,APR_HepG2_MitoMembPot_72h_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -52,APR_HepG2_MitoMembPot_72h_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -52,APR_HepG2_MitoMembPot_72h_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -52,APR_HepG2_MitoMembPot_72h_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -53,APR_HepG2_MitoticArrest_72h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -53,APR_HepG2_MitoticArrest_72h_dn,41,sustained AhR activation leading to rodent liver tumors,853,"Changes/Inhibition, Cellular Homeostasis and Apoptosis",KE -53,APR_HepG2_MitoticArrest_72h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -53,APR_HepG2_MitoticArrest_72h_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -53,APR_HepG2_MitoticArrest_72h_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -53,APR_HepG2_MitoticArrest_72h_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -53,APR_HepG2_MitoticArrest_72h_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -53,APR_HepG2_MitoticArrest_72h_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -53,APR_HepG2_MitoticArrest_72h_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -54,APR_HepG2_MitoticArrest_72h_up,41,sustained AhR activation leading to rodent liver tumors,853,"Changes/Inhibition, Cellular Homeostasis and Apoptosis",KE -57,APR_HepG2_OxidativeStress_72h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -57,APR_HepG2_OxidativeStress_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -57,APR_HepG2_OxidativeStress_72h_dn,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -57,APR_HepG2_OxidativeStress_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -57,APR_HepG2_OxidativeStress_72h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -57,APR_HepG2_OxidativeStress_72h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -57,APR_HepG2_OxidativeStress_72h_dn,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -57,APR_HepG2_OxidativeStress_72h_dn,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -57,APR_HepG2_OxidativeStress_72h_dn,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -57,APR_HepG2_OxidativeStress_72h_dn,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -57,APR_HepG2_OxidativeStress_72h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -57,APR_HepG2_OxidativeStress_72h_dn,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -57,APR_HepG2_OxidativeStress_72h_dn,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -57,APR_HepG2_OxidativeStress_72h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -58,APR_HepG2_OxidativeStress_72h_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -58,APR_HepG2_OxidativeStress_72h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -58,APR_HepG2_OxidativeStress_72h_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -58,APR_HepG2_OxidativeStress_72h_up,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -58,APR_HepG2_OxidativeStress_72h_up,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -58,APR_HepG2_OxidativeStress_72h_up,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -58,APR_HepG2_OxidativeStress_72h_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -58,APR_HepG2_OxidativeStress_72h_up,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -58,APR_HepG2_OxidativeStress_72h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -58,APR_HepG2_OxidativeStress_72h_up,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -58,APR_HepG2_OxidativeStress_72h_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -58,APR_HepG2_OxidativeStress_72h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -58,APR_HepG2_OxidativeStress_72h_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -58,APR_HepG2_OxidativeStress_72h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -61,APR_HepG2_StressKinase_72h_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -61,APR_HepG2_StressKinase_72h_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -61,APR_HepG2_StressKinase_72h_dn,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -61,APR_HepG2_StressKinase_72h_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -61,APR_HepG2_StressKinase_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -61,APR_HepG2_StressKinase_72h_dn,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -61,APR_HepG2_StressKinase_72h_dn,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -61,APR_HepG2_StressKinase_72h_dn,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -61,APR_HepG2_StressKinase_72h_dn,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -61,APR_HepG2_StressKinase_72h_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -61,APR_HepG2_StressKinase_72h_dn,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -61,APR_HepG2_StressKinase_72h_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -61,APR_HepG2_StressKinase_72h_dn,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -61,APR_HepG2_StressKinase_72h_dn,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -62,APR_HepG2_StressKinase_72h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1278,ROS formation,KE -62,APR_HepG2_StressKinase_72h_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),249,"Production, Reactive oxygen species",KE -62,APR_HepG2_StressKinase_72h_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1279,"Increase, Oxidative Stress / Activation, PMK-1 P38 MAPK",KE -62,APR_HepG2_StressKinase_72h_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -62,APR_HepG2_StressKinase_72h_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1088,"Increased, Oxidative Stress",KE -62,APR_HepG2_StressKinase_72h_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -62,APR_HepG2_StressKinase_72h_up,186,unknown MIE leading to renal failure and mortality,1088,"Increased, Oxidative Stress",KE -62,APR_HepG2_StressKinase_72h_up,200,Estrogen receptor activation leading to breast cancer,1088,"Increased, Oxidative Stress",KE -62,APR_HepG2_StressKinase_72h_up,220,chronic cyp2E1 activation leading to liver cancer,1392,Oxidative Stress,KE -62,APR_HepG2_StressKinase_72h_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,1392,Oxidative Stress,KE -62,APR_HepG2_StressKinase_72h_up,238,Excessive reactive oxygen species production leading to reproductive failure,257,"Increase, Reactive oxygen species production",MIE -62,APR_HepG2_StressKinase_72h_up,31,oxidation of iron in hemoglobin leading to hematotoxicity,211,"Propagation, Oxidative stress",KE -62,APR_HepG2_StressKinase_72h_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,257,"Increase, Reactive oxygen species production",MIE -62,APR_HepG2_StressKinase_72h_up,26,Calcium-mediated neuronal ROS production and energy imbalance,257,"Increase, Reactive oxygen species production",KE -63,ATG_Ahr_CIS_up,150,Aryl hydrocarbon receptor activation leading to embryolethality via cardiotoxicity,18,"Activation, AhR",MIE -63,ATG_Ahr_CIS_up,41,sustained AhR activation leading to rodent liver tumors,165,"Activation, Long term AHR receptor driven direct and indirect gene expression changes",MIE -63,ATG_Ahr_CIS_up,21,AhR activation leading to early life stage mortality,18,"Activation, AhR",MIE -63,ATG_Ahr_CIS_up,57,AhR activation leading to hepatic steatosis,18,"Activation, AhR",MIE -63,ATG_Ahr_CIS_up,131,aryl hydrocarbon receptor activation leading to uroporphyria,18,"Activation, AhR",MIE -70,ATG_DR4_LXR_CIS_up,34,LXR activation to liver steatosis,167,"Activation, LXR",MIE -70,ATG_DR4_LXR_CIS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,483,"Activation, LXR alpha",KE -70,ATG_DR4_LXR_CIS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,167,"Activation, LXR",MIE -75,ATG_ERE_CIS_up,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -75,ATG_ERE_CIS_up,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -75,ATG_ERE_CIS_up,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -77,ATG_FoxA2_CIS_up,60,"NR1I2 (pregnane X receptor, PXR) suppression leading to hepatic steatosis",471,"Inhibition, FoxA2",KE -81,ATG_GRE_CIS_up,214,Network of SSRIs,122,"Activation, Glucocorticoid Receptor",KE -81,ATG_GRE_CIS_up,14,Glucocorticoid receptor activation leading to increased disease susceptibility,122,"Activation, Glucocorticoid Receptor",MIE -81,ATG_GRE_CIS_up,71,Modultaion of adult leydig cell function subsequent to glucocorticoid activation,650,"Stimulation of adult Leydig cells via the Adrenal Corticosterone, Increased Glucocorticoid",MIE -81,ATG_GRE_CIS_up,64,Glucocorticoid receptor (GR) mediated adult leydig cell dysfunction leading to decreased male fertility,494,"Glucocorticoid Receptor Agonist, Activation",MIE -82,ATG_HIF1a_CIS_up,123,Unknown MIE leading to reproductive dysfunction via increased HIF-1alpha transcription,802,"Increased, HIF-1 alpha transcription",KE -82,ATG_HIF1a_CIS_up,122,prolyl hydorxylase inhibition leading to reproductive dysfunction via increased HIF1 heterdimer formation,799,"Increased, HIF-1 heterodimer",KE -82,ATG_HIF1a_CIS_up,123,Unknown MIE leading to reproductive dysfunction via increased HIF-1alpha transcription,799,"Increased, HIF-1 heterodimer",KE -85,ATG_IR1_CIS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,479,"Activation, NR1H4",MIE -94,ATG_NF_kB_CIS_up,14,Glucocorticoid receptor activation leading to increased disease susceptibility,202,"Inhibition, Nuclear factor kappa B (NF-kB)",KE -97,ATG_NRF2_ARE_CIS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,478,"Activation, NRF2",MIE -101,ATG_PBREM_CIS_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),715,"Activation, Constitutive androstane receptor",MIE -102,ATG_PPRE_CIS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,468,"Inhibition, PPAR alpha",MIE -102,ATG_PPRE_CIS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,227,"Activation, PPARa",KE -102,ATG_PPRE_CIS_up,6,Antagonist binding to PPARalpha leading to body-weight loss,998,"Binding of antagonist, PPAR alpha",MIE -102,ATG_PPRE_CIS_up,51,PPARalpha activation leading to impaired fertility in adult male rodents,227,"Activation, PPARa",MIE -102,ATG_PPRE_CIS_up,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -102,ATG_PPRE_CIS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -102,ATG_PPRE_CIS_up,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -102,ATG_PPRE_CIS_up,18,PPARalpha activation in utero leading to impaired fertility in males,227,"Activation, PPARa",MIE -102,ATG_PPRE_CIS_up,72,Epigenetic Modification of PPARg leading to Adipogenesis,1028,"Activation of specific nuclear receptors, PPAR-gamma activation",KE -102,ATG_PPRE_CIS_up,163,"PPARgamma activation leading to sarcomas in rats, mice and hamsters",1028,"Activation of specific nuclear receptors, PPAR-gamma activation",MIE -102,ATG_PPRE_CIS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -102,ATG_PPRE_CIS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -102,ATG_PPRE_CIS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -102,ATG_PPRE_CIS_up,37,PPARalpha-dependent liver cancer,227,"Activation, PPARa",MIE -103,ATG_PXRE_CIS_up,60,"NR1I2 (pregnane X receptor, PXR) suppression leading to hepatic steatosis",245,"Activation, PXR/SXR",MIE -103,ATG_PXRE_CIS_up,8,"Upregulation of thyroid hormone catabolism via activation of hepatic nuclear receptors, and subsequent adverse neurodevelopmental outcomes in mammals",239,"Activation, Pregnane-X receptor, NR1l2",MIE -103,ATG_PXRE_CIS_up,11,Percellome Toxicogenomics Approach for AOP Building: Case Study on Pentachlorophenol,245,"Activation, PXR/SXR",MIE -107,ATG_SREBP_CIS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,457,"Activation, SREBF1",KE -107,ATG_SREBP_CIS_up,34,LXR activation to liver steatosis,264,"Activation, SREBP-1c",KE -107,ATG_SREBP_CIS_up,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1284,"Up Regulation, SREBF2",KE -107,ATG_SREBP_CIS_up,62,AKT2 activation leading to hepatic steatosis,457,"Activation, SREBF1",KE -112,ATG_TGFb_CIS_up,208,Janus kinase (JAK)/Signal transducer and activator of transcription (STAT) and Transforming growth factor (TGF)-beta pathways activation leading to reproductive failure,1283,"Activation, TGF-beta pathway",KE -112,ATG_TGFb_CIS_up,38,protein alkylation leading to liver fibrosis,276,"Up Regulation, TGFbeta1 expression",KE -112,ATG_TGFb_CIS_up,206,peroxisome proliferatory-activated receptor gamma inactivation leading to lung fibrosis,1271,Activation of TGF-β signaling,KE -115,ATG_AR_TRANS_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),785,"Activation, Androgen receptor",MIE -115,ATG_AR_TRANS_up,23,Androgen receptor agonism leading to reproductive dysfunction,25,"Agonism, Androgen receptor",MIE -116,ATG_CAR_TRANS_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),715,"Activation, Constitutive androstane receptor",MIE -117,ATG_ERa_TRANS_up,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -117,ATG_ERa_TRANS_up,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -117,ATG_ERa_TRANS_up,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -117,ATG_ERa_TRANS_up,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -117,ATG_ERa_TRANS_up,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -117,ATG_ERa_TRANS_up,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -117,ATG_ERa_TRANS_up,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -120,ATG_FXR_TRANS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,479,"Activation, NR1H4",MIE -122,ATG_GR_TRANS_up,14,Glucocorticoid receptor activation leading to increased disease susceptibility,122,"Activation, Glucocorticoid Receptor",MIE -122,ATG_GR_TRANS_up,64,Glucocorticoid receptor (GR) mediated adult leydig cell dysfunction leading to decreased male fertility,494,"Glucocorticoid Receptor Agonist, Activation",MIE -122,ATG_GR_TRANS_up,71,Modultaion of adult leydig cell function subsequent to glucocorticoid activation,650,"Stimulation of adult Leydig cells via the Adrenal Corticosterone, Increased Glucocorticoid",MIE -122,ATG_GR_TRANS_up,214,Network of SSRIs,122,"Activation, Glucocorticoid Receptor",KE -125,ATG_LXRa_TRANS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,167,"Activation, LXR",MIE -125,ATG_LXRa_TRANS_up,34,LXR activation to liver steatosis,167,"Activation, LXR",MIE -125,ATG_LXRa_TRANS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,483,"Activation, LXR alpha",KE -126,ATG_LXRb_TRANS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,483,"Activation, LXR alpha",KE -126,ATG_LXRb_TRANS_up,34,LXR activation to liver steatosis,167,"Activation, LXR",MIE -126,ATG_LXRb_TRANS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,167,"Activation, LXR",MIE -132,ATG_PPARa_TRANS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,468,"Inhibition, PPAR alpha",MIE -132,ATG_PPARa_TRANS_up,6,Antagonist binding to PPARalpha leading to body-weight loss,998,"Binding of antagonist, PPAR alpha",MIE -132,ATG_PPARa_TRANS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -132,ATG_PPARa_TRANS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -132,ATG_PPARa_TRANS_up,18,PPARalpha activation in utero leading to impaired fertility in males,227,"Activation, PPARa",MIE -132,ATG_PPARa_TRANS_up,61,NFE2L2/FXR activation leading to hepatic steatosis,227,"Activation, PPARa",KE -132,ATG_PPARa_TRANS_up,37,PPARalpha-dependent liver cancer,227,"Activation, PPARa",MIE -132,ATG_PPARa_TRANS_up,51,PPARalpha activation leading to impaired fertility in adult male rodents,227,"Activation, PPARa",MIE -132,ATG_PPARa_TRANS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -134,ATG_PPARg_TRANS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -134,ATG_PPARg_TRANS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -134,ATG_PPARg_TRANS_up,72,Epigenetic Modification of PPARg leading to Adipogenesis,1028,"Activation of specific nuclear receptors, PPAR-gamma activation",KE -134,ATG_PPARg_TRANS_up,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -134,ATG_PPARg_TRANS_up,163,"PPARgamma activation leading to sarcomas in rats, mice and hamsters",1028,"Activation of specific nuclear receptors, PPAR-gamma activation",MIE -134,ATG_PPARg_TRANS_up,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -134,ATG_PPARg_TRANS_up,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -134,ATG_PPARg_TRANS_up,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -135,ATG_PXR_TRANS_up,60,"NR1I2 (pregnane X receptor, PXR) suppression leading to hepatic steatosis",245,"Activation, PXR/SXR",MIE -135,ATG_PXR_TRANS_up,11,Percellome Toxicogenomics Approach for AOP Building: Case Study on Pentachlorophenol,245,"Activation, PXR/SXR",MIE -135,ATG_PXR_TRANS_up,8,"Upregulation of thyroid hormone catabolism via activation of hepatic nuclear receptors, and subsequent adverse neurodevelopmental outcomes in mammals",239,"Activation, Pregnane-X receptor, NR1l2",MIE -151,BSK_3C_IL8_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -152,BSK_3C_IL8_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -153,BSK_3C_MCP1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -154,BSK_3C_MCP1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -155,BSK_3C_MIG_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -156,BSK_3C_MIG_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -157,BSK_3C_Proliferation_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -157,BSK_3C_Proliferation_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -157,BSK_3C_Proliferation_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -157,BSK_3C_Proliferation_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -157,BSK_3C_Proliferation_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -157,BSK_3C_Proliferation_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -157,BSK_3C_Proliferation_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -157,BSK_3C_Proliferation_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -157,BSK_3C_Proliferation_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -157,BSK_3C_Proliferation_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -157,BSK_3C_Proliferation_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -157,BSK_3C_Proliferation_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -157,BSK_3C_Proliferation_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -158,BSK_3C_Proliferation_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -158,BSK_3C_Proliferation_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -158,BSK_3C_Proliferation_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -158,BSK_3C_Proliferation_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -158,BSK_3C_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -158,BSK_3C_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -158,BSK_3C_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -158,BSK_3C_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -159,BSK_3C_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -159,BSK_3C_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -159,BSK_3C_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -159,BSK_3C_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -159,BSK_3C_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -159,BSK_3C_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -159,BSK_3C_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -159,BSK_3C_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -159,BSK_3C_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -159,BSK_3C_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -159,BSK_3C_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -159,BSK_3C_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -159,BSK_3C_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -163,BSK_3C_TissueFactor_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -164,BSK_3C_TissueFactor_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -165,BSK_3C_uPAR_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -166,BSK_3C_uPAR_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -171,BSK_4H_Eotaxin3_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -172,BSK_4H_Eotaxin3_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -173,BSK_4H_MCP1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -174,BSK_4H_MCP1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -177,BSK_4H_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -177,BSK_4H_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -177,BSK_4H_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -177,BSK_4H_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -177,BSK_4H_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -177,BSK_4H_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -177,BSK_4H_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -177,BSK_4H_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -177,BSK_4H_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -177,BSK_4H_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -177,BSK_4H_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -177,BSK_4H_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -177,BSK_4H_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -179,BSK_4H_uPAR_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -180,BSK_4H_uPAR_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -183,BSK_4H_VEGFRII_down,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,305,"Inhibition, VegfR2",MIE -183,BSK_4H_VEGFRII_down,150,Aryl hydrocarbon receptor activation leading to embryolethality via cardiotoxicity,948,"reduced production, VEGF",KE -184,BSK_4H_VEGFRII_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,305,"Inhibition, VegfR2",MIE -187,BSK_BE3C_IL1a_down,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -187,BSK_BE3C_IL1a_down,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -187,BSK_BE3C_IL1a_down,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -187,BSK_BE3C_IL1a_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -188,BSK_BE3C_IL1a_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -188,BSK_BE3C_IL1a_up,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -188,BSK_BE3C_IL1a_up,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -188,BSK_BE3C_IL1a_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -189,BSK_BE3C_IP10_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -190,BSK_BE3C_IP10_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -191,BSK_BE3C_MIG_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -192,BSK_BE3C_MIG_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -195,BSK_BE3C_PAI1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -196,BSK_BE3C_PAI1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -197,BSK_BE3C_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -197,BSK_BE3C_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -197,BSK_BE3C_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -197,BSK_BE3C_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -197,BSK_BE3C_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -197,BSK_BE3C_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -197,BSK_BE3C_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -197,BSK_BE3C_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -197,BSK_BE3C_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -197,BSK_BE3C_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -197,BSK_BE3C_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -197,BSK_BE3C_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -197,BSK_BE3C_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -199,BSK_BE3C_TGFb1_down,38,protein alkylation leading to liver fibrosis,276,"Up Regulation, TGFbeta1 expression",KE -200,BSK_BE3C_TGFb1_up,206,peroxisome proliferatory-activated receptor gamma inactivation leading to lung fibrosis,1271,Activation of TGF-β signaling,KE -200,BSK_BE3C_TGFb1_up,208,Janus kinase (JAK)/Signal transducer and activator of transcription (STAT) and Transforming growth factor (TGF)-beta pathways activation leading to reproductive failure,1283,"Activation, TGF-beta pathway",KE -200,BSK_BE3C_TGFb1_up,38,protein alkylation leading to liver fibrosis,276,"Up Regulation, TGFbeta1 expression",KE -205,BSK_BE3C_uPAR_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -206,BSK_BE3C_uPAR_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -209,BSK_CASM3C_IL6_down,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -209,BSK_CASM3C_IL6_down,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -209,BSK_CASM3C_IL6_down,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -209,BSK_CASM3C_IL6_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -210,BSK_CASM3C_IL6_up,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -210,BSK_CASM3C_IL6_up,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -210,BSK_CASM3C_IL6_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -210,BSK_CASM3C_IL6_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -211,BSK_CASM3C_IL8_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -212,BSK_CASM3C_IL8_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -215,BSK_CASM3C_MCP1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -216,BSK_CASM3C_MCP1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -217,BSK_CASM3C_MCSF_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -218,BSK_CASM3C_MCSF_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -219,BSK_CASM3C_MIG_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -220,BSK_CASM3C_MIG_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -221,BSK_CASM3C_Proliferation_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -221,BSK_CASM3C_Proliferation_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -221,BSK_CASM3C_Proliferation_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -221,BSK_CASM3C_Proliferation_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -221,BSK_CASM3C_Proliferation_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -221,BSK_CASM3C_Proliferation_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -221,BSK_CASM3C_Proliferation_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -221,BSK_CASM3C_Proliferation_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -221,BSK_CASM3C_Proliferation_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -221,BSK_CASM3C_Proliferation_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -221,BSK_CASM3C_Proliferation_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -221,BSK_CASM3C_Proliferation_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -221,BSK_CASM3C_Proliferation_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -222,BSK_CASM3C_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -222,BSK_CASM3C_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -222,BSK_CASM3C_Proliferation_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -222,BSK_CASM3C_Proliferation_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -222,BSK_CASM3C_Proliferation_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -222,BSK_CASM3C_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -222,BSK_CASM3C_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -222,BSK_CASM3C_Proliferation_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -225,BSK_CASM3C_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -225,BSK_CASM3C_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -225,BSK_CASM3C_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -225,BSK_CASM3C_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -225,BSK_CASM3C_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -225,BSK_CASM3C_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -225,BSK_CASM3C_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -225,BSK_CASM3C_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -225,BSK_CASM3C_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -225,BSK_CASM3C_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -225,BSK_CASM3C_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -225,BSK_CASM3C_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -225,BSK_CASM3C_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -229,BSK_CASM3C_TissueFactor_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -230,BSK_CASM3C_TissueFactor_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -231,BSK_CASM3C_uPAR_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -232,BSK_CASM3C_uPAR_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -239,BSK_hDFCGF_IL8_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -240,BSK_hDFCGF_IL8_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -241,BSK_hDFCGF_IP10_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -242,BSK_hDFCGF_IP10_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -243,BSK_hDFCGF_MCSF_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -244,BSK_hDFCGF_MCSF_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -245,BSK_hDFCGF_MIG_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -246,BSK_hDFCGF_MIG_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -249,BSK_hDFCGF_PAI1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -250,BSK_hDFCGF_PAI1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -251,BSK_hDFCGF_Proliferation_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -251,BSK_hDFCGF_Proliferation_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -251,BSK_hDFCGF_Proliferation_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -251,BSK_hDFCGF_Proliferation_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -251,BSK_hDFCGF_Proliferation_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -251,BSK_hDFCGF_Proliferation_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -251,BSK_hDFCGF_Proliferation_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -251,BSK_hDFCGF_Proliferation_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -251,BSK_hDFCGF_Proliferation_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -251,BSK_hDFCGF_Proliferation_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -251,BSK_hDFCGF_Proliferation_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -251,BSK_hDFCGF_Proliferation_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -251,BSK_hDFCGF_Proliferation_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -252,BSK_hDFCGF_Proliferation_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -252,BSK_hDFCGF_Proliferation_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -252,BSK_hDFCGF_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -252,BSK_hDFCGF_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -252,BSK_hDFCGF_Proliferation_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -252,BSK_hDFCGF_Proliferation_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -252,BSK_hDFCGF_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -252,BSK_hDFCGF_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -253,BSK_hDFCGF_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -253,BSK_hDFCGF_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -253,BSK_hDFCGF_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -253,BSK_hDFCGF_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -253,BSK_hDFCGF_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -253,BSK_hDFCGF_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -253,BSK_hDFCGF_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -253,BSK_hDFCGF_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -253,BSK_hDFCGF_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -253,BSK_hDFCGF_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -253,BSK_hDFCGF_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -253,BSK_hDFCGF_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -253,BSK_hDFCGF_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -261,BSK_KF3CT_IL1a_down,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -261,BSK_KF3CT_IL1a_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -261,BSK_KF3CT_IL1a_down,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -261,BSK_KF3CT_IL1a_down,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -262,BSK_KF3CT_IL1a_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -262,BSK_KF3CT_IL1a_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -262,BSK_KF3CT_IL1a_up,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -262,BSK_KF3CT_IL1a_up,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -263,BSK_KF3CT_IP10_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -264,BSK_KF3CT_IP10_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -265,BSK_KF3CT_MCP1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -266,BSK_KF3CT_MCP1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -269,BSK_KF3CT_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -269,BSK_KF3CT_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -269,BSK_KF3CT_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -269,BSK_KF3CT_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -269,BSK_KF3CT_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -269,BSK_KF3CT_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -269,BSK_KF3CT_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -269,BSK_KF3CT_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -269,BSK_KF3CT_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -269,BSK_KF3CT_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -269,BSK_KF3CT_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -269,BSK_KF3CT_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -269,BSK_KF3CT_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -271,BSK_KF3CT_TGFb1_down,38,protein alkylation leading to liver fibrosis,276,"Up Regulation, TGFbeta1 expression",KE -272,BSK_KF3CT_TGFb1_up,208,Janus kinase (JAK)/Signal transducer and activator of transcription (STAT) and Transforming growth factor (TGF)-beta pathways activation leading to reproductive failure,1283,"Activation, TGF-beta pathway",KE -272,BSK_KF3CT_TGFb1_up,206,peroxisome proliferatory-activated receptor gamma inactivation leading to lung fibrosis,1271,Activation of TGF-β signaling,KE -272,BSK_KF3CT_TGFb1_up,38,protein alkylation leading to liver fibrosis,276,"Up Regulation, TGFbeta1 expression",KE -277,BSK_LPS_CD40_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -278,BSK_LPS_CD40_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -281,BSK_LPS_IL1a_down,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -281,BSK_LPS_IL1a_down,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -281,BSK_LPS_IL1a_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -281,BSK_LPS_IL1a_down,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -282,BSK_LPS_IL1a_up,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -282,BSK_LPS_IL1a_up,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -282,BSK_LPS_IL1a_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -282,BSK_LPS_IL1a_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -283,BSK_LPS_IL8_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -284,BSK_LPS_IL8_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -285,BSK_LPS_MCP1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -286,BSK_LPS_MCP1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -287,BSK_LPS_MCSF_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -288,BSK_LPS_MCSF_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -291,BSK_LPS_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -291,BSK_LPS_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -291,BSK_LPS_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -291,BSK_LPS_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -291,BSK_LPS_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -291,BSK_LPS_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -291,BSK_LPS_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -291,BSK_LPS_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -291,BSK_LPS_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -291,BSK_LPS_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -291,BSK_LPS_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -291,BSK_LPS_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -291,BSK_LPS_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -293,BSK_LPS_TissueFactor_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -294,BSK_LPS_TissueFactor_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -295,BSK_LPS_TNFa_down,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -295,BSK_LPS_TNFa_down,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -295,BSK_LPS_TNFa_down,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -295,BSK_LPS_TNFa_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -296,BSK_LPS_TNFa_up,27,Cholestatic liver injury induced by inhibition of the bile salt export pump (ABCB11),87,"Release, Cytokine",KE -296,BSK_LPS_TNFa_up,144,Lysosomal damage leading to liver inflammation,87,"Release, Cytokine",KE -296,BSK_LPS_TNFa_up,39,Sensitisation of the respiratory tract induced by covalent binding of low molecular weight organic chemicals to proteins,151,"Activation, Inflammatory cytokines, chemokines, cytoprotective gene pathways",KE -296,BSK_LPS_TNFa_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -299,BSK_SAg_CD38_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -300,BSK_SAg_CD38_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -301,BSK_SAg_CD40_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -302,BSK_SAg_CD40_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -303,BSK_SAg_CD69_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -304,BSK_SAg_CD69_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -307,BSK_SAg_IL8_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -308,BSK_SAg_IL8_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -309,BSK_SAg_MCP1_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -310,BSK_SAg_MCP1_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -311,BSK_SAg_MIG_down,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -312,BSK_SAg_MIG_up,173,Increased substance interaction with the resident cell membrane components leading to lung fibrosis,1496,"Increased, secretion of proinflammatory and profibrotic mediators",KE -313,BSK_SAg_PBMCCytotoxicity_down,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -313,BSK_SAg_PBMCCytotoxicity_down,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -313,BSK_SAg_PBMCCytotoxicity_down,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -313,BSK_SAg_PBMCCytotoxicity_down,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -313,BSK_SAg_PBMCCytotoxicity_down,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -313,BSK_SAg_PBMCCytotoxicity_down,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -313,BSK_SAg_PBMCCytotoxicity_down,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -313,BSK_SAg_PBMCCytotoxicity_down,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -314,BSK_SAg_PBMCCytotoxicity_up,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -314,BSK_SAg_PBMCCytotoxicity_up,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -314,BSK_SAg_PBMCCytotoxicity_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -314,BSK_SAg_PBMCCytotoxicity_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -314,BSK_SAg_PBMCCytotoxicity_up,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -314,BSK_SAg_PBMCCytotoxicity_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -314,BSK_SAg_PBMCCytotoxicity_up,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -314,BSK_SAg_PBMCCytotoxicity_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -314,BSK_SAg_PBMCCytotoxicity_up,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -314,BSK_SAg_PBMCCytotoxicity_up,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -314,BSK_SAg_PBMCCytotoxicity_up,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -314,BSK_SAg_PBMCCytotoxicity_up,205,AoP from chemical insult to cell death,1263,Necrosis,AO -314,BSK_SAg_PBMCCytotoxicity_up,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -315,BSK_SAg_Proliferation_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -315,BSK_SAg_Proliferation_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -315,BSK_SAg_Proliferation_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -315,BSK_SAg_Proliferation_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -315,BSK_SAg_Proliferation_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -315,BSK_SAg_Proliferation_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -315,BSK_SAg_Proliferation_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -315,BSK_SAg_Proliferation_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -315,BSK_SAg_Proliferation_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -315,BSK_SAg_Proliferation_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -315,BSK_SAg_Proliferation_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -315,BSK_SAg_Proliferation_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -315,BSK_SAg_Proliferation_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -316,BSK_SAg_Proliferation_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -316,BSK_SAg_Proliferation_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -316,BSK_SAg_Proliferation_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -316,BSK_SAg_Proliferation_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -316,BSK_SAg_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -316,BSK_SAg_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -316,BSK_SAg_Proliferation_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -316,BSK_SAg_Proliferation_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -317,BSK_SAg_SRB_down,205,AoP from chemical insult to cell death,1263,Necrosis,AO -317,BSK_SAg_SRB_down,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -317,BSK_SAg_SRB_down,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -317,BSK_SAg_SRB_down,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -317,BSK_SAg_SRB_down,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -317,BSK_SAg_SRB_down,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -317,BSK_SAg_SRB_down,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -317,BSK_SAg_SRB_down,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -317,BSK_SAg_SRB_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -317,BSK_SAg_SRB_down,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -317,BSK_SAg_SRB_down,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -317,BSK_SAg_SRB_down,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -317,BSK_SAg_SRB_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -319,NVS_ADME_hCYP19A1,123,Unknown MIE leading to reproductive dysfunction via increased HIF-1alpha transcription,800,"Decreased, Aromatase (Cyp19a1) mRNA",KE -319,NVS_ADME_hCYP19A1,122,prolyl hydorxylase inhibition leading to reproductive dysfunction via increased HIF1 heterdimer formation,800,"Decreased, Aromatase (Cyp19a1) mRNA",KE -319,NVS_ADME_hCYP19A1,25,Aromatase inhibtion leading to reproductive dysfunction (in fish),36,"Inhibition, Aromatase",MIE -319,NVS_ADME_hCYP19A1,7,Aromatase (Cyp19a1) reduction leading to impaired fertility in adult female,408,"reduction in ovarian granulosa cells, Aromatase (Cyp19a1)",MIE -319,NVS_ADME_hCYP19A1,153,Aromatase inhibition leadin to ovulation inhibition and decreased fertility in female rats,964,"Inhibition of Aromatase Enzyme, Chemical exposure during critical window of estrous cycle between diestrus 2 and proestrus inhibits aromatase conversion of testosterone to estradiol",MIE -321,NVS_ADME_hCYP1A1,57,AhR activation leading to hepatic steatosis,80,"Up Regulation, CYP1A1",KE -322,NVS_ADME_hCYP1A1_Activator,57,AhR activation leading to hepatic steatosis,80,"Up Regulation, CYP1A1",KE -323,NVS_ADME_hCYP1A2,131,aryl hydrocarbon receptor activation leading to uroporphyria,850,"Induction, CYP1A2/CYP1A5",KE -324,NVS_ADME_hCYP1A2_Activator,131,aryl hydrocarbon receptor activation leading to uroporphyria,850,"Induction, CYP1A2/CYP1A5",KE -351,NVS_ADME_rCYP1A1,57,AhR activation leading to hepatic steatosis,80,"Up Regulation, CYP1A1",KE -352,NVS_ADME_rCYP1A1_Activator,57,AhR activation leading to hepatic steatosis,80,"Up Regulation, CYP1A1",KE -353,NVS_ADME_rCYP1A2,131,aryl hydrocarbon receptor activation leading to uroporphyria,850,"Induction, CYP1A2/CYP1A5",KE -354,NVS_ADME_rCYP1A2_Activator,131,aryl hydrocarbon receptor activation leading to uroporphyria,850,"Induction, CYP1A2/CYP1A5",KE -381,NVS_ENZ_hAChE,16,Acetylcholinesterase inhibition leading to acute mortality,12,"Inhibition, Acetylcholinesterase (AchE)",MIE -382,NVS_ENZ_hAChE_Activator,16,Acetylcholinesterase inhibition leading to acute mortality,12,"Inhibition, Acetylcholinesterase (AchE)",MIE -387,NVS_ENZ_hAKT2,62,AKT2 activation leading to hepatic steatosis,484,"Activation, AKT2",KE -388,NVS_ENZ_hAKT2_Activator,62,AKT2 activation leading to hepatic steatosis,484,"Activation, AKT2",KE -457,NVS_ENZ_hIKKa,14,Glucocorticoid receptor activation leading to increased disease susceptibility,145,"Induction, IKB inhibitory protein",KE -458,NVS_ENZ_hIKKa_Activator,14,Glucocorticoid receptor activation leading to increased disease susceptibility,145,"Induction, IKB inhibitory protein",KE -464,NVS_ENZ_hJak2_Activator,208,Janus kinase (JAK)/Signal transducer and activator of transcription (STAT) and Transforming growth factor (TGF)-beta pathways activation leading to reproductive failure,1282,"Activation, JAK/STAT pathway",KE -576,NVS_ENZ_hVEGFR1_Activator,150,Aryl hydrocarbon receptor activation leading to embryolethality via cardiotoxicity,948,"reduced production, VEGF",KE -577,NVS_ENZ_hVEGFR2,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,305,"Inhibition, VegfR2",MIE -578,NVS_ENZ_hVEGFR2_Activator,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,305,"Inhibition, VegfR2",MIE -583,NVS_ENZ_oCOX1,28,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -583,NVS_ENZ_oCOX1,101,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of pheromone release,79,"Inhibition, Cyclooxygenase activity",MIE -583,NVS_ENZ_oCOX1,100,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of female spawning behavior,79,"Inhibition, Cyclooxygenase activity",MIE -583,NVS_ENZ_oCOX1,63,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -583,NVS_ENZ_oCOX1,103,cyclooxygenase inhibition leading to reproductive dysfunction via interference with spindle assembly checkpoint,79,"Inhibition, Cyclooxygenase activity",MIE -583,NVS_ENZ_oCOX1,102,cyclooxygenase inhibition leading to reproductive dysfunction via interference with meiotic prophase I/ metaphase I transition,79,"Inhibition, Cyclooxygenase activity",MIE -583,NVS_ENZ_oCOX1,217,gastric ulcer formation,1371,"Inhibition, PTGS-2 (Prostaglandin-endoperoxide synthase 2)",KE -583,NVS_ENZ_oCOX1,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,1103,"Inhibition, Cyclooxygenase 1 activity",MIE -584,NVS_ENZ_oCOX1_Activator,63,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -584,NVS_ENZ_oCOX1_Activator,100,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of female spawning behavior,79,"Inhibition, Cyclooxygenase activity",MIE -584,NVS_ENZ_oCOX1_Activator,103,cyclooxygenase inhibition leading to reproductive dysfunction via interference with spindle assembly checkpoint,79,"Inhibition, Cyclooxygenase activity",MIE -584,NVS_ENZ_oCOX1_Activator,102,cyclooxygenase inhibition leading to reproductive dysfunction via interference with meiotic prophase I/ metaphase I transition,79,"Inhibition, Cyclooxygenase activity",MIE -584,NVS_ENZ_oCOX1_Activator,101,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of pheromone release,79,"Inhibition, Cyclooxygenase activity",MIE -584,NVS_ENZ_oCOX1_Activator,28,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -584,NVS_ENZ_oCOX1_Activator,217,gastric ulcer formation,1371,"Inhibition, PTGS-2 (Prostaglandin-endoperoxide synthase 2)",KE -585,NVS_ENZ_oCOX2,100,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of female spawning behavior,79,"Inhibition, Cyclooxygenase activity",MIE -585,NVS_ENZ_oCOX2,217,gastric ulcer formation,1371,"Inhibition, PTGS-2 (Prostaglandin-endoperoxide synthase 2)",KE -585,NVS_ENZ_oCOX2,103,cyclooxygenase inhibition leading to reproductive dysfunction via interference with spindle assembly checkpoint,79,"Inhibition, Cyclooxygenase activity",MIE -585,NVS_ENZ_oCOX2,101,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of pheromone release,79,"Inhibition, Cyclooxygenase activity",MIE -585,NVS_ENZ_oCOX2,63,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -585,NVS_ENZ_oCOX2,102,cyclooxygenase inhibition leading to reproductive dysfunction via interference with meiotic prophase I/ metaphase I transition,79,"Inhibition, Cyclooxygenase activity",MIE -585,NVS_ENZ_oCOX2,28,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -586,NVS_ENZ_oCOX2_Activator,28,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -586,NVS_ENZ_oCOX2_Activator,217,gastric ulcer formation,1371,"Inhibition, PTGS-2 (Prostaglandin-endoperoxide synthase 2)",KE -586,NVS_ENZ_oCOX2_Activator,103,cyclooxygenase inhibition leading to reproductive dysfunction via interference with spindle assembly checkpoint,79,"Inhibition, Cyclooxygenase activity",MIE -586,NVS_ENZ_oCOX2_Activator,102,cyclooxygenase inhibition leading to reproductive dysfunction via interference with meiotic prophase I/ metaphase I transition,79,"Inhibition, Cyclooxygenase activity",MIE -586,NVS_ENZ_oCOX2_Activator,101,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of pheromone release,79,"Inhibition, Cyclooxygenase activity",MIE -586,NVS_ENZ_oCOX2_Activator,63,Cyclooxygenase inhibition leading to reproductive failure,79,"Inhibition, Cyclooxygenase activity",MIE -586,NVS_ENZ_oCOX2_Activator,100,cyclogoxygenase inhibition leading to reproductive dysfunction via inhibition of female spawning behavior,79,"Inhibition, Cyclooxygenase activity",MIE -593,NVS_ENZ_rAChE,16,Acetylcholinesterase inhibition leading to acute mortality,12,"Inhibition, Acetylcholinesterase (AchE)",MIE -594,NVS_ENZ_rAChE_Activator,16,Acetylcholinesterase inhibition leading to acute mortality,12,"Inhibition, Acetylcholinesterase (AchE)",MIE -603,NVS_ENZ_rMAOAP,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -603,NVS_ENZ_rMAOAP,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -603,NVS_ENZ_rMAOAP,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -603,NVS_ENZ_rMAOAP,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -603,NVS_ENZ_rMAOAP,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -603,NVS_ENZ_rMAOAP,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -603,NVS_ENZ_rMAOAP,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -603,NVS_ENZ_rMAOAP,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -603,NVS_ENZ_rMAOAP,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -603,NVS_ENZ_rMAOAP,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -603,NVS_ENZ_rMAOAP,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -603,NVS_ENZ_rMAOAP,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -604,NVS_ENZ_rMAOAP_Activator,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -604,NVS_ENZ_rMAOAP_Activator,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -604,NVS_ENZ_rMAOAP_Activator,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -604,NVS_ENZ_rMAOAP_Activator,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -604,NVS_ENZ_rMAOAP_Activator,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -604,NVS_ENZ_rMAOAP_Activator,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -604,NVS_ENZ_rMAOAP_Activator,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -604,NVS_ENZ_rMAOAP_Activator,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -604,NVS_ENZ_rMAOAP_Activator,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -604,NVS_ENZ_rMAOAP_Activator,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -614,NVS_GPCR_g5HT4,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -614,NVS_GPCR_g5HT4,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -614,NVS_GPCR_g5HT4,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -614,NVS_GPCR_g5HT4,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -614,NVS_GPCR_g5HT4,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -617,NVS_GPCR_gH2,99,histamine (H2) receptor antagonism leading to reduced survival,638,"Antagonism, Histamine Receptor (H2)",MIE -622,NVS_GPCR_h5HT2A,214,Network of SSRIs,1336,"Activation, 5-HT2A (Serotonin 2A)",KE -622,NVS_GPCR_h5HT2A,226,SSRI to hypertension,1336,"Activation, 5-HT2A (Serotonin 2A)",KE -622,NVS_GPCR_h5HT2A,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -622,NVS_GPCR_h5HT2A,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -622,NVS_GPCR_h5HT2A,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -622,NVS_GPCR_h5HT2A,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -622,NVS_GPCR_h5HT2A,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -623,NVS_GPCR_h5HT5A,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -623,NVS_GPCR_h5HT5A,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -623,NVS_GPCR_h5HT5A,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -623,NVS_GPCR_h5HT5A,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -623,NVS_GPCR_h5HT5A,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -624,NVS_GPCR_h5HT6,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -624,NVS_GPCR_h5HT6,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -624,NVS_GPCR_h5HT6,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -624,NVS_GPCR_h5HT6,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -624,NVS_GPCR_h5HT6,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -625,NVS_GPCR_h5HT7,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -625,NVS_GPCR_h5HT7,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -625,NVS_GPCR_h5HT7,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -625,NVS_GPCR_h5HT7,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -625,NVS_GPCR_h5HT7,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -628,NVS_GPCR_hAdra2A,126,alpha-noradrenergic antagonism leads to reduced fecundity via delayed ovulation,849,"Inhibition, Antgonism of NE receptor",MIE -631,NVS_GPCR_hAdrb2,164,Beta-2 adrenergic agonist activity leading to mesovarian leiomyomas in the rat and mouse,1038,"Activation, beta-2 adrenergic receptor",MIE -636,NVS_GPCR_hDRD2s,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),746,"Increase, Dopaminergic activity",MIE -652,NVS_GPCR_hOpiate_mu,234,Mu opioid receptor agonism leading to analgesia via Ca channel inhibition,1425,Mu Opioid Receptor Agonism,MIE -652,NVS_GPCR_hOpiate_mu,233,Mu opioid receptor agonism leading to analgesia via K channel opening,1425,Mu Opioid Receptor Agonism,MIE -659,NVS_GPCR_p5HT2C,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -659,NVS_GPCR_p5HT2C,33,Kidney toxicity induced by activation of 5HT2C,9,"Activation, 5HT2c",MIE -659,NVS_GPCR_p5HT2C,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -659,NVS_GPCR_p5HT2C,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -659,NVS_GPCR_p5HT2C,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -659,NVS_GPCR_p5HT2C,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -660,NVS_GPCR_r5HT_NonSelective,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -660,NVS_GPCR_r5HT_NonSelective,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -660,NVS_GPCR_r5HT_NonSelective,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -660,NVS_GPCR_r5HT_NonSelective,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -660,NVS_GPCR_r5HT_NonSelective,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -661,NVS_GPCR_r5HT1_NonSelective,235,Serotonin 1A receptor agonism leading to anti-depressant activity via K channel opening,1431,Serotonin 1A Receptor Agonism,MIE -661,NVS_GPCR_r5HT1_NonSelective,236,Serotonin 1A receptor agonism leading to anti-depressant activity via Ca channel inhibition,1431,Serotonin 1A Receptor Agonism,MIE -661,NVS_GPCR_r5HT1_NonSelective,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -661,NVS_GPCR_r5HT1_NonSelective,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -661,NVS_GPCR_r5HT1_NonSelective,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -661,NVS_GPCR_r5HT1_NonSelective,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -661,NVS_GPCR_r5HT1_NonSelective,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -666,NVS_GPCR_rAdra2_NonSelective,126,alpha-noradrenergic antagonism leads to reduced fecundity via delayed ovulation,849,"Inhibition, Antgonism of NE receptor",MIE -669,NVS_GPCR_rGABBR,161,Glutamate-gated chloride channel activation leading to neurotransmission inhibition associated mortality,1018,"Activation, Glutamate-gated chloride channels",MIE -672,NVS_GPCR_rH3,99,histamine (H2) receptor antagonism leading to reduced survival,638,"Antagonism, Histamine Receptor (H2)",MIE -679,NVS_GPCR_rOpiate_NonSelective,234,Mu opioid receptor agonism leading to analgesia via Ca channel inhibition,1425,Mu Opioid Receptor Agonism,MIE -679,NVS_GPCR_rOpiate_NonSelective,233,Mu opioid receptor agonism leading to analgesia via K channel opening,1425,Mu Opioid Receptor Agonism,MIE -680,NVS_GPCR_rOpiate_NonSelectiveNa,234,Mu opioid receptor agonism leading to analgesia via Ca channel inhibition,1425,Mu Opioid Receptor Agonism,MIE -680,NVS_GPCR_rOpiate_NonSelectiveNa,233,Mu opioid receptor agonism leading to analgesia via K channel opening,1425,Mu Opioid Receptor Agonism,MIE -686,NVS_IC_hKhERGCh,95,Ether-a-go-go (ERG) voltage-gated potassium channel inhibition leading to reduced survival,593,"Inhibition, Ether-a-go-go (ERG) voltage-gated potassium channel",MIE -693,NVS_IC_rNaCh_site2,197,sodium channel (Nav1.1) inhibition leading to population decline,584,"Inhibition, sodium channel",MIE -693,NVS_IC_rNaCh_site2,94,sodium channel inhibition leading to congenital malformations,584,"Inhibition, sodium channel",MIE -693,NVS_IC_rNaCh_site2,93,sodium channel inhibition leading to increased predation,584,"Inhibition, sodium channel",MIE -693,NVS_IC_rNaCh_site2,96,axonal sodium channel modulation leading to acute mortality,598,"modulation, sodium channel",MIE -693,NVS_IC_rNaCh_site2,91,Sodium channel inhibition leading to reduced survival,584,"Inhibition, sodium channel",MIE -694,NVS_LGIC_bGABAR_Agonist,10,Binding to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures,667,"Binding at picrotoxin site, iGABAR chloride channel",MIE -694,NVS_LGIC_bGABAR_Agonist,160,Ionotropic gamma-aminbutyric acid receptor activation mediated neurotransmissino inhibition leading to mortality,762,"Activation, ionotropic GABA Receptor chloride channel",MIE -695,NVS_LGIC_bGABARa1,215,Molecular events lead to epilepsy,1358,"Repress, GABA-A receptor",KE -695,NVS_LGIC_bGABARa1,160,Ionotropic gamma-aminbutyric acid receptor activation mediated neurotransmissino inhibition leading to mortality,762,"Activation, ionotropic GABA Receptor chloride channel",MIE -695,NVS_LGIC_bGABARa1,10,Binding to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures,667,"Binding at picrotoxin site, iGABAR chloride channel",MIE -696,NVS_LGIC_bGABARa5,215,Molecular events lead to epilepsy,1358,"Repress, GABA-A receptor",KE -696,NVS_LGIC_bGABARa5,160,Ionotropic gamma-aminbutyric acid receptor activation mediated neurotransmissino inhibition leading to mortality,762,"Activation, ionotropic GABA Receptor chloride channel",MIE -696,NVS_LGIC_bGABARa5,10,Binding to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures,667,"Binding at picrotoxin site, iGABAR chloride channel",MIE -697,NVS_LGIC_h5HT3,225,serotonin transporter activation to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -697,NVS_LGIC_h5HT3,224,Serotonin transporter activation to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -697,NVS_LGIC_h5HT3,222,mental stress to agitation,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -697,NVS_LGIC_h5HT3,221,mental stress to depression,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -697,NVS_LGIC_h5HT3,214,Network of SSRIs,1328,"Inactivated, 5-HTR (serotonin receptors)",KE -700,NVS_LGIC_rGABAR_NonSelective,215,Molecular events lead to epilepsy,1358,"Repress, GABA-A receptor",KE -700,NVS_LGIC_rGABAR_NonSelective,160,Ionotropic gamma-aminbutyric acid receptor activation mediated neurotransmissino inhibition leading to mortality,762,"Activation, ionotropic GABA Receptor chloride channel",MIE -700,NVS_LGIC_rGABAR_NonSelective,10,Binding to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures,667,"Binding at picrotoxin site, iGABAR chloride channel",MIE -701,NVS_LGIC_rGABARa6,160,Ionotropic gamma-aminbutyric acid receptor activation mediated neurotransmissino inhibition leading to mortality,762,"Activation, ionotropic GABA Receptor chloride channel",MIE -701,NVS_LGIC_rGABARa6,215,Molecular events lead to epilepsy,1358,"Repress, GABA-A receptor",KE -701,NVS_LGIC_rGABARa6,10,Binding to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures,667,"Binding at picrotoxin site, iGABAR chloride channel",MIE -702,NVS_LGIC_rGluNMDA_Agonist,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,201,"Binding of antagonist, NMDA receptors",MIE -702,NVS_LGIC_rGluNMDA_Agonist,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,201,"Binding of antagonist, NMDA receptors",MIE -703,NVS_LGIC_rGluNMDA_MK801_Agonist,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,201,"Binding of antagonist, NMDA receptors",MIE -703,NVS_LGIC_rGluNMDA_MK801_Agonist,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,201,"Binding of antagonist, NMDA receptors",MIE -706,NVS_MP_hPBR,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -706,NVS_MP_hPBR,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -706,NVS_MP_hPBR,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -706,NVS_MP_hPBR,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -706,NVS_MP_hPBR,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -706,NVS_MP_hPBR,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -706,NVS_MP_hPBR,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -706,NVS_MP_hPBR,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -706,NVS_MP_hPBR,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -706,NVS_MP_hPBR,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -706,NVS_MP_hPBR,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -706,NVS_MP_hPBR,18,PPARalpha activation in utero leading to impaired fertility in males,289,"Decrease, Translocator protein (TSPO)",KE -707,NVS_MP_rPBR,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,18,PPARalpha activation in utero leading to impaired fertility in males,289,"Decrease, Translocator protein (TSPO)",KE -707,NVS_MP_rPBR,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -707,NVS_MP_rPBR,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -707,NVS_MP_rPBR,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -707,NVS_MP_rPBR,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -707,NVS_MP_rPBR,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -707,NVS_MP_rPBR,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -707,NVS_MP_rPBR,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -707,NVS_MP_rPBR,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -707,NVS_MP_rPBR,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -707,NVS_MP_rPBR,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -707,NVS_MP_rPBR,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -708,NVS_NR_bER,165,Antiestrogen activity leading to ovarian adenomas and granular cell tumors in the mouse,1046,"Suppression, Estrogen receptor (ER) activity",KE -708,NVS_NR_bER,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -708,NVS_NR_bER,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -708,NVS_NR_bER,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -708,NVS_NR_bER,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -711,NVS_NR_hAR,111,Decrease in androgen receptor activity leading to Leydig cell tumors (in rat),742,"Decreased, Androgen receptor activity",MIE -711,NVS_NR_hAR,19,Androgen receptor antagonism leading to adverse effects in the male foetus (mammals),27,"N/A, Androgen receptor, Antagonism",MIE -712,NVS_NR_hCAR_Agonist,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),715,"Activation, Constitutive androstane receptor",MIE -713,NVS_NR_hCAR_Antagonist,58,NR1I3 (CAR) suppression leading to hepatic steatosis,456,"Suppression, Constitutive androstane receptor, NR1l3",MIE -714,NVS_NR_hER,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -714,NVS_NR_hER,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -714,NVS_NR_hER,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -714,NVS_NR_hER,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -714,NVS_NR_hER,165,Antiestrogen activity leading to ovarian adenomas and granular cell tumors in the mouse,1046,"Suppression, Estrogen receptor (ER) activity",KE -715,NVS_NR_hFXR_Agonist,61,NFE2L2/FXR activation leading to hepatic steatosis,479,"Activation, NR1H4",MIE -717,NVS_NR_hGR,14,Glucocorticoid receptor activation leading to increased disease susceptibility,122,"Activation, Glucocorticoid Receptor",MIE -717,NVS_NR_hGR,214,Network of SSRIs,122,"Activation, Glucocorticoid Receptor",KE -717,NVS_NR_hGR,64,Glucocorticoid receptor (GR) mediated adult leydig cell dysfunction leading to decreased male fertility,494,"Glucocorticoid Receptor Agonist, Activation",MIE -717,NVS_NR_hGR,71,Modultaion of adult leydig cell function subsequent to glucocorticoid activation,650,"Stimulation of adult Leydig cells via the Adrenal Corticosterone, Increased Glucocorticoid",MIE -719,NVS_NR_hPPARg,206,peroxisome proliferatory-activated receptor gamma inactivation leading to lung fibrosis,1270,Inactivation of PPARγ,MIE -719,NVS_NR_hPPARg,163,"PPARgamma activation leading to sarcomas in rats, mice and hamsters",1028,"Activation of specific nuclear receptors, PPAR-gamma activation",MIE -719,NVS_NR_hPPARg,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -719,NVS_NR_hPPARg,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -719,NVS_NR_hPPARg,72,Epigenetic Modification of PPARg leading to Adipogenesis,1028,"Activation of specific nuclear receptors, PPAR-gamma activation",KE -719,NVS_NR_hPPARg,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -719,NVS_NR_hPPARg,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -719,NVS_NR_hPPARg,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -719,NVS_NR_hPPARg,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -721,NVS_NR_hPXR,11,Percellome Toxicogenomics Approach for AOP Building: Case Study on Pentachlorophenol,245,"Activation, PXR/SXR",MIE -721,NVS_NR_hPXR,60,"NR1I2 (pregnane X receptor, PXR) suppression leading to hepatic steatosis",245,"Activation, PXR/SXR",MIE -721,NVS_NR_hPXR,8,"Upregulation of thyroid hormone catabolism via activation of hepatic nuclear receptors, and subsequent adverse neurodevelopmental outcomes in mammals",239,"Activation, Pregnane-X receptor, NR1l2",MIE -725,NVS_NR_mERa,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -725,NVS_NR_mERa,165,Antiestrogen activity leading to ovarian adenomas and granular cell tumors in the mouse,1046,"Suppression, Estrogen receptor (ER) activity",KE -725,NVS_NR_mERa,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -725,NVS_NR_mERa,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -725,NVS_NR_mERa,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -726,NVS_NR_rAR,111,Decrease in androgen receptor activity leading to Leydig cell tumors (in rat),742,"Decreased, Androgen receptor activity",MIE -726,NVS_NR_rAR,19,Androgen receptor antagonism leading to adverse effects in the male foetus (mammals),27,"N/A, Androgen receptor, Antagonism",MIE -729,NVS_OR_hFKBP12,154,Inhibition of Calcineurin Activity leading to Impaired T-Cell dependent antibody response,980,"Inhibition, Calcineurin Activity",MIE -734,NVS_TR_hSERT,224,Serotonin transporter activation to depression,1397,"Increased, serotonin transporter activity",MIE -734,NVS_TR_hSERT,223,Serotonin transporter activation to seizure,1397,"Increased, serotonin transporter activity",MIE -734,NVS_TR_hSERT,225,serotonin transporter activation to agitation,1397,"Increased, serotonin transporter activity",KE -734,NVS_TR_hSERT,98,5-hydroxytryptamine transporter (5-HTT;SERT) inhibition leading to decreased shelter seeking and increased predation,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -734,NVS_TR_hSERT,221,mental stress to depression,1317,"Decreased, serotonin transporter activity",KE -734,NVS_TR_hSERT,204,5-hydroxytryptamine transporter inhibition leading to increased reproductive success and population increase,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -734,NVS_TR_hSERT,222,mental stress to agitation,1317,"Decreased, serotonin transporter activity",KE -734,NVS_TR_hSERT,97,5-hydroxytryptamine transporter (5-HTT;SERT) inhibition leading to increased predation,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -734,NVS_TR_hSERT,226,SSRI to hypertension,1317,"Decreased, serotonin transporter activity",MIE -734,NVS_TR_hSERT,214,Network of SSRIs,1317,"Decreased, serotonin transporter activity",KE -734,NVS_TR_hSERT,214,Network of SSRIs,1316,"Inhibit, serotonin transporter activity",KE -734,NVS_TR_hSERT,195,5-hydroxytryptamine transporter (5-HTT) inhibition leading to population increase,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -734,NVS_TR_hSERT,203,5-hydroxytryptamine transporter (5-HTT) inhibition leading to decreased reproductive success and population decline,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -737,NVS_TR_rSERT,98,5-hydroxytryptamine transporter (5-HTT;SERT) inhibition leading to decreased shelter seeking and increased predation,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -737,NVS_TR_rSERT,223,Serotonin transporter activation to seizure,1397,"Increased, serotonin transporter activity",MIE -737,NVS_TR_rSERT,226,SSRI to hypertension,1317,"Decreased, serotonin transporter activity",MIE -737,NVS_TR_rSERT,97,5-hydroxytryptamine transporter (5-HTT;SERT) inhibition leading to increased predation,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -737,NVS_TR_rSERT,222,mental stress to agitation,1317,"Decreased, serotonin transporter activity",KE -737,NVS_TR_rSERT,225,serotonin transporter activation to agitation,1397,"Increased, serotonin transporter activity",KE -737,NVS_TR_rSERT,195,5-hydroxytryptamine transporter (5-HTT) inhibition leading to population increase,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -737,NVS_TR_rSERT,203,5-hydroxytryptamine transporter (5-HTT) inhibition leading to decreased reproductive success and population decline,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -737,NVS_TR_rSERT,204,5-hydroxytryptamine transporter inhibition leading to increased reproductive success and population increase,619,"Inhibition, 5-hydroxytryptamine transporter (5-HTT; SERT)",MIE -737,NVS_TR_rSERT,221,mental stress to depression,1317,"Decreased, serotonin transporter activity",KE -737,NVS_TR_rSERT,224,Serotonin transporter activation to depression,1397,"Increased, serotonin transporter activity",MIE -737,NVS_TR_rSERT,214,Network of SSRIs,1317,"Decreased, serotonin transporter activity",KE -737,NVS_TR_rSERT,214,Network of SSRIs,1316,"Inhibit, serotonin transporter activity",KE -739,OT_AR_ARELUC_AG_1440,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),785,"Activation, Androgen receptor",MIE -739,OT_AR_ARELUC_AG_1440,23,Androgen receptor agonism leading to reproductive dysfunction,25,"Agonism, Androgen receptor",MIE -740,OT_AR_ARSRC1_0480,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),785,"Activation, Androgen receptor",MIE -740,OT_AR_ARSRC1_0480,23,Androgen receptor agonism leading to reproductive dysfunction,25,"Agonism, Androgen receptor",MIE -741,OT_AR_ARSRC1_0960,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),785,"Activation, Androgen receptor",MIE -741,OT_AR_ARSRC1_0960,23,Androgen receptor agonism leading to reproductive dysfunction,25,"Agonism, Androgen receptor",MIE -742,OT_ER_ERaERa_0480,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -742,OT_ER_ERaERa_0480,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -742,OT_ER_ERaERa_0480,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -742,OT_ER_ERaERa_0480,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -742,OT_ER_ERaERa_0480,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -742,OT_ER_ERaERa_0480,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -742,OT_ER_ERaERa_0480,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -743,OT_ER_ERaERa_1440,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -743,OT_ER_ERaERa_1440,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -743,OT_ER_ERaERa_1440,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -743,OT_ER_ERaERa_1440,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -743,OT_ER_ERaERa_1440,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -743,OT_ER_ERaERa_1440,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -743,OT_ER_ERaERa_1440,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -744,OT_ER_ERaERb_0480,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -744,OT_ER_ERaERb_0480,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -744,OT_ER_ERaERb_0480,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -744,OT_ER_ERaERb_0480,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -744,OT_ER_ERaERb_0480,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -744,OT_ER_ERaERb_0480,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -744,OT_ER_ERaERb_0480,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -745,OT_ER_ERaERb_1440,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -745,OT_ER_ERaERb_1440,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -745,OT_ER_ERaERb_1440,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -745,OT_ER_ERaERb_1440,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -745,OT_ER_ERaERb_1440,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -745,OT_ER_ERaERb_1440,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -745,OT_ER_ERaERb_1440,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -746,OT_ER_ERbERb_0480,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -746,OT_ER_ERbERb_0480,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -746,OT_ER_ERbERb_0480,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -746,OT_ER_ERbERb_0480,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -746,OT_ER_ERbERb_0480,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -746,OT_ER_ERbERb_0480,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -746,OT_ER_ERbERb_0480,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -747,OT_ER_ERbERb_1440,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -747,OT_ER_ERbERb_1440,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -747,OT_ER_ERbERb_1440,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -747,OT_ER_ERbERb_1440,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -747,OT_ER_ERbERb_1440,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -747,OT_ER_ERbERb_1440,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -747,OT_ER_ERbERb_1440,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -750,OT_ERa_EREGFP_0120,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -750,OT_ERa_EREGFP_0120,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -750,OT_ERa_EREGFP_0120,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -750,OT_ERa_EREGFP_0120,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -750,OT_ERa_EREGFP_0120,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -750,OT_ERa_EREGFP_0120,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -750,OT_ERa_EREGFP_0120,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -751,OT_ERa_EREGFP_0480,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -751,OT_ERa_EREGFP_0480,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -751,OT_ERa_EREGFP_0480,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -751,OT_ERa_EREGFP_0480,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -751,OT_ERa_EREGFP_0480,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -751,OT_ERa_EREGFP_0480,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -751,OT_ERa_EREGFP_0480,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -753,OT_FXR_FXRSRC1_0480,61,NFE2L2/FXR activation leading to hepatic steatosis,479,"Activation, NR1H4",MIE -754,OT_FXR_FXRSRC1_1440,61,NFE2L2/FXR activation leading to hepatic steatosis,479,"Activation, NR1H4",MIE -757,OT_PPARg_PPARgSRC1_0480,72,Epigenetic Modification of PPARg leading to Adipogenesis,1028,"Activation of specific nuclear receptors, PPAR-gamma activation",KE -757,OT_PPARg_PPARgSRC1_0480,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -757,OT_PPARg_PPARgSRC1_0480,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -757,OT_PPARg_PPARgSRC1_0480,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -757,OT_PPARg_PPARgSRC1_0480,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -757,OT_PPARg_PPARgSRC1_0480,163,"PPARgamma activation leading to sarcomas in rats, mice and hamsters",1028,"Activation of specific nuclear receptors, PPAR-gamma activation",MIE -757,OT_PPARg_PPARgSRC1_0480,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -757,OT_PPARg_PPARgSRC1_0480,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -758,OT_PPARg_PPARgSRC1_1440,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -758,OT_PPARg_PPARgSRC1_1440,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -758,OT_PPARg_PPARgSRC1_1440,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -758,OT_PPARg_PPARgSRC1_1440,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -758,OT_PPARg_PPARgSRC1_1440,163,"PPARgamma activation leading to sarcomas in rats, mice and hamsters",1028,"Activation of specific nuclear receptors, PPAR-gamma activation",MIE -758,OT_PPARg_PPARgSRC1_1440,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -758,OT_PPARg_PPARgSRC1_1440,72,Epigenetic Modification of PPARg leading to Adipogenesis,1028,"Activation of specific nuclear receptors, PPAR-gamma activation",KE -758,OT_PPARg_PPARgSRC1_1440,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -761,TOX21_AR_BLA_Agonist_ratio,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),785,"Activation, Androgen receptor",MIE -761,TOX21_AR_BLA_Agonist_ratio,23,Androgen receptor agonism leading to reproductive dysfunction,25,"Agonism, Androgen receptor",MIE -762,TOX21_AR_BLA_Antagonist_ratio,111,Decrease in androgen receptor activity leading to Leydig cell tumors (in rat),742,"Decreased, Androgen receptor activity",MIE -762,TOX21_AR_BLA_Antagonist_ratio,19,Androgen receptor antagonism leading to adverse effects in the male foetus (mammals),27,"N/A, Androgen receptor, Antagonism",MIE -763,TOX21_AR_BLA_Antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -763,TOX21_AR_BLA_Antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -763,TOX21_AR_BLA_Antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -763,TOX21_AR_BLA_Antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -763,TOX21_AR_BLA_Antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -763,TOX21_AR_BLA_Antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -763,TOX21_AR_BLA_Antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -763,TOX21_AR_BLA_Antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -763,TOX21_AR_BLA_Antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -763,TOX21_AR_BLA_Antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -763,TOX21_AR_BLA_Antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -763,TOX21_AR_BLA_Antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -763,TOX21_AR_BLA_Antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -764,TOX21_AR_LUC_MDAKB2_Agonist,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),785,"Activation, Androgen receptor",MIE -764,TOX21_AR_LUC_MDAKB2_Agonist,23,Androgen receptor agonism leading to reproductive dysfunction,25,"Agonism, Androgen receptor",MIE -765,TOX21_AR_LUC_MDAKB2_Antagonist,111,Decrease in androgen receptor activity leading to Leydig cell tumors (in rat),742,"Decreased, Androgen receptor activity",MIE -765,TOX21_AR_LUC_MDAKB2_Antagonist,19,Androgen receptor antagonism leading to adverse effects in the male foetus (mammals),27,"N/A, Androgen receptor, Antagonism",MIE -767,TOX21_Aromatase_Inhibition,7,Aromatase (Cyp19a1) reduction leading to impaired fertility in adult female,408,"reduction in ovarian granulosa cells, Aromatase (Cyp19a1)",MIE -767,TOX21_Aromatase_Inhibition,122,prolyl hydorxylase inhibition leading to reproductive dysfunction via increased HIF1 heterdimer formation,800,"Decreased, Aromatase (Cyp19a1) mRNA",KE -767,TOX21_Aromatase_Inhibition,123,Unknown MIE leading to reproductive dysfunction via increased HIF-1alpha transcription,800,"Decreased, Aromatase (Cyp19a1) mRNA",KE -767,TOX21_Aromatase_Inhibition,25,Aromatase inhibtion leading to reproductive dysfunction (in fish),36,"Inhibition, Aromatase",MIE -767,TOX21_Aromatase_Inhibition,153,Aromatase inhibition leadin to ovulation inhibition and decreased fertility in female rats,964,"Inhibition of Aromatase Enzyme, Chemical exposure during critical window of estrous cycle between diestrus 2 and proestrus inhibits aromatase conversion of testosterone to estradiol",MIE -785,TOX21_ERa_BLA_Agonist_ratio,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -785,TOX21_ERa_BLA_Agonist_ratio,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -785,TOX21_ERa_BLA_Agonist_ratio,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -785,TOX21_ERa_BLA_Agonist_ratio,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -785,TOX21_ERa_BLA_Agonist_ratio,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -785,TOX21_ERa_BLA_Agonist_ratio,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -786,TOX21_ERa_BLA_Antagonist_ratio,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -786,TOX21_ERa_BLA_Antagonist_ratio,165,Antiestrogen activity leading to ovarian adenomas and granular cell tumors in the mouse,1046,"Suppression, Estrogen receptor (ER) activity",KE -787,TOX21_ERa_BLA_Antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -787,TOX21_ERa_BLA_Antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -787,TOX21_ERa_BLA_Antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -787,TOX21_ERa_BLA_Antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -787,TOX21_ERa_BLA_Antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -787,TOX21_ERa_BLA_Antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -787,TOX21_ERa_BLA_Antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -787,TOX21_ERa_BLA_Antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -787,TOX21_ERa_BLA_Antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -787,TOX21_ERa_BLA_Antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -787,TOX21_ERa_BLA_Antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -787,TOX21_ERa_BLA_Antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -787,TOX21_ERa_BLA_Antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -788,TOX21_ERa_LUC_BG1_Agonist,167,Early-life estrogen receptor activity leading to endometrial carcinoma in the mouse,1064,"prepubertal increase, Estrogen receptor (ER) activity",MIE -788,TOX21_ERa_LUC_BG1_Agonist,200,Estrogen receptor activation leading to breast cancer,1181,"Activation, Estrogen receptor",MIE -788,TOX21_ERa_LUC_BG1_Agonist,52,ER agonism leading to skewed sex ratios due to altered sexual differentiation in males,111,"Agonism, Estrogen receptor",MIE -788,TOX21_ERa_LUC_BG1_Agonist,29,Estrogen receptor agonism leading to reproductive dysfunction,111,"Agonism, Estrogen receptor",MIE -788,TOX21_ERa_LUC_BG1_Agonist,112,Increased dopaminergic activity leading to endometrial adenocarcinomas (in Wistar rat),748,"Increased, Estrogen receptor (ER) activity",KE -788,TOX21_ERa_LUC_BG1_Agonist,53,ER agonism leading to reduced survival due to renal failure,111,"Agonism, Estrogen receptor",MIE -789,TOX21_ERa_LUC_BG1_Antagonist,30,Estrogen receptor antagonism leading to reproductive dysfunction,112,"Antagonism, Estrogen receptor",MIE -789,TOX21_ERa_LUC_BG1_Antagonist,165,Antiestrogen activity leading to ovarian adenomas and granular cell tumors in the mouse,1046,"Suppression, Estrogen receptor (ER) activity",KE -793,TOX21_GR_BLA_Agonist_ratio,64,Glucocorticoid receptor (GR) mediated adult leydig cell dysfunction leading to decreased male fertility,494,"Glucocorticoid Receptor Agonist, Activation",MIE -793,TOX21_GR_BLA_Agonist_ratio,214,Network of SSRIs,122,"Activation, Glucocorticoid Receptor",KE -793,TOX21_GR_BLA_Agonist_ratio,71,Modultaion of adult leydig cell function subsequent to glucocorticoid activation,650,"Stimulation of adult Leydig cells via the Adrenal Corticosterone, Increased Glucocorticoid",MIE -793,TOX21_GR_BLA_Agonist_ratio,14,Glucocorticoid receptor activation leading to increased disease susceptibility,122,"Activation, Glucocorticoid Receptor",MIE -794,TOX21_GR_BLA_Antagonist_ratio,214,Network of SSRIs,122,"Activation, Glucocorticoid Receptor",KE -794,TOX21_GR_BLA_Antagonist_ratio,14,Glucocorticoid receptor activation leading to increased disease susceptibility,122,"Activation, Glucocorticoid Receptor",MIE -794,TOX21_GR_BLA_Antagonist_ratio,71,Modultaion of adult leydig cell function subsequent to glucocorticoid activation,650,"Stimulation of adult Leydig cells via the Adrenal Corticosterone, Increased Glucocorticoid",MIE -794,TOX21_GR_BLA_Antagonist_ratio,64,Glucocorticoid receptor (GR) mediated adult leydig cell dysfunction leading to decreased male fertility,494,"Glucocorticoid Receptor Agonist, Activation",MIE -795,TOX21_GR_BLA_Antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -795,TOX21_GR_BLA_Antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -795,TOX21_GR_BLA_Antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -795,TOX21_GR_BLA_Antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -795,TOX21_GR_BLA_Antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -795,TOX21_GR_BLA_Antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -795,TOX21_GR_BLA_Antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -795,TOX21_GR_BLA_Antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -795,TOX21_GR_BLA_Antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -795,TOX21_GR_BLA_Antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -795,TOX21_GR_BLA_Antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -795,TOX21_GR_BLA_Antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -795,TOX21_GR_BLA_Antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -797,TOX21_MMP_ratio_down,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -797,TOX21_MMP_ratio_down,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -797,TOX21_MMP_ratio_down,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -797,TOX21_MMP_ratio_down,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -797,TOX21_MMP_ratio_down,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -797,TOX21_MMP_ratio_down,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -797,TOX21_MMP_ratio_down,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -797,TOX21_MMP_ratio_down,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -797,TOX21_MMP_ratio_down,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -797,TOX21_MMP_ratio_down,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -797,TOX21_MMP_ratio_down,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -799,TOX21_MMP_viability,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -799,TOX21_MMP_viability,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -799,TOX21_MMP_viability,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -799,TOX21_MMP_viability,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -799,TOX21_MMP_viability,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -799,TOX21_MMP_viability,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -799,TOX21_MMP_viability,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -799,TOX21_MMP_viability,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -799,TOX21_MMP_viability,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -799,TOX21_MMP_viability,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -799,TOX21_MMP_viability,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -799,TOX21_MMP_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -799,TOX21_MMP_viability,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -799,TOX21_MMP_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -799,TOX21_MMP_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -799,TOX21_MMP_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -799,TOX21_MMP_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -799,TOX21_MMP_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -799,TOX21_MMP_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -799,TOX21_MMP_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -799,TOX21_MMP_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -799,TOX21_MMP_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -799,TOX21_MMP_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -799,TOX21_MMP_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -802,TOX21_PPARg_BLA_Agonist_ratio,163,"PPARgamma activation leading to sarcomas in rats, mice and hamsters",1028,"Activation of specific nuclear receptors, PPAR-gamma activation",MIE -802,TOX21_PPARg_BLA_Agonist_ratio,72,Epigenetic Modification of PPARg leading to Adipogenesis,1028,"Activation of specific nuclear receptors, PPAR-gamma activation",KE -802,TOX21_PPARg_BLA_Agonist_ratio,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -802,TOX21_PPARg_BLA_Agonist_ratio,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -802,TOX21_PPARg_BLA_Agonist_ratio,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -806,TOX21_AhR_LUC_Agonist,41,sustained AhR activation leading to rodent liver tumors,165,"Activation, Long term AHR receptor driven direct and indirect gene expression changes",MIE -806,TOX21_AhR_LUC_Agonist,21,AhR activation leading to early life stage mortality,18,"Activation, AhR",MIE -806,TOX21_AhR_LUC_Agonist,57,AhR activation leading to hepatic steatosis,18,"Activation, AhR",MIE -806,TOX21_AhR_LUC_Agonist,131,aryl hydrocarbon receptor activation leading to uroporphyria,18,"Activation, AhR",MIE -806,TOX21_AhR_LUC_Agonist,150,Aryl hydrocarbon receptor activation leading to embryolethality via cardiotoxicity,18,"Activation, AhR",MIE -1110,TOX21_ARE_BLA_agonist_ratio,61,NFE2L2/FXR activation leading to hepatic steatosis,478,"Activation, NRF2",MIE -1119,TOX21_FXR_BLA_agonist_ratio,61,NFE2L2/FXR activation leading to hepatic steatosis,479,"Activation, NR1H4",MIE -1121,TOX21_FXR_BLA_antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1121,TOX21_FXR_BLA_antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1121,TOX21_FXR_BLA_antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1121,TOX21_FXR_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1121,TOX21_FXR_BLA_antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1121,TOX21_FXR_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1121,TOX21_FXR_BLA_antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1121,TOX21_FXR_BLA_antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1121,TOX21_FXR_BLA_antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1121,TOX21_FXR_BLA_antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1121,TOX21_FXR_BLA_antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1121,TOX21_FXR_BLA_antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1121,TOX21_FXR_BLA_antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1126,TOX21_PPARd_BLA_antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1126,TOX21_PPARd_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1126,TOX21_PPARd_BLA_antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1126,TOX21_PPARd_BLA_antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1126,TOX21_PPARd_BLA_antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1126,TOX21_PPARd_BLA_antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1126,TOX21_PPARd_BLA_antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1126,TOX21_PPARd_BLA_antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1126,TOX21_PPARd_BLA_antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1126,TOX21_PPARd_BLA_antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1126,TOX21_PPARd_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1126,TOX21_PPARd_BLA_antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1126,TOX21_PPARd_BLA_antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1127,TOX21_PPARg_BLA_antagonist_ratio,206,peroxisome proliferatory-activated receptor gamma inactivation leading to lung fibrosis,1270,Inactivation of PPARγ,MIE -1127,TOX21_PPARg_BLA_antagonist_ratio,72,Epigenetic Modification of PPARg leading to Adipogenesis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -1127,TOX21_PPARg_BLA_antagonist_ratio,58,NR1I3 (CAR) suppression leading to hepatic steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -1127,TOX21_PPARg_BLA_antagonist_ratio,34,LXR activation to liver steatosis,228,peroxisome proliferator activated receptor promoter demethylation,MIE -1128,TOX21_PPARg_BLA_antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1128,TOX21_PPARg_BLA_antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1128,TOX21_PPARg_BLA_antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1128,TOX21_PPARg_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1128,TOX21_PPARg_BLA_antagonist_viability,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,232,"Decreased, PPAR-beta activation",MIE -1128,TOX21_PPARg_BLA_antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1128,TOX21_PPARg_BLA_antagonist_viability,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,233,"Decreased, PPAR-gamma activation",MIE -1128,TOX21_PPARg_BLA_antagonist_viability,36,Peroxisomal fatty acid beta-oxidation inhibition leading to steatosis,231,"Decreased, PPAR-alpha activation",MIE -1128,TOX21_PPARg_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1128,TOX21_PPARg_BLA_antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1128,TOX21_PPARg_BLA_antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1128,TOX21_PPARg_BLA_antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1128,TOX21_PPARg_BLA_antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1128,TOX21_PPARg_BLA_antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1128,TOX21_PPARg_BLA_antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1128,TOX21_PPARg_BLA_antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1133,TOX21_VDR_BLA_antagonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1133,TOX21_VDR_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1133,TOX21_VDR_BLA_antagonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1133,TOX21_VDR_BLA_antagonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1133,TOX21_VDR_BLA_antagonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1133,TOX21_VDR_BLA_antagonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1133,TOX21_VDR_BLA_antagonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1133,TOX21_VDR_BLA_antagonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1133,TOX21_VDR_BLA_antagonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1133,TOX21_VDR_BLA_antagonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1133,TOX21_VDR_BLA_antagonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1133,TOX21_VDR_BLA_antagonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1133,TOX21_VDR_BLA_antagonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1137,APR_Hepat_Apoptosis_1hr_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -1137,APR_Hepat_Apoptosis_1hr_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1137,APR_Hepat_Apoptosis_1hr_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -1137,APR_Hepat_Apoptosis_1hr_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1137,APR_Hepat_Apoptosis_1hr_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1137,APR_Hepat_Apoptosis_1hr_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -1137,APR_Hepat_Apoptosis_1hr_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -1137,APR_Hepat_Apoptosis_1hr_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -1139,APR_Hepat_CellLoss_1hr_dn,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1139,APR_Hepat_CellLoss_1hr_dn,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1139,APR_Hepat_CellLoss_1hr_dn,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1139,APR_Hepat_CellLoss_1hr_dn,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1139,APR_Hepat_CellLoss_1hr_dn,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1139,APR_Hepat_CellLoss_1hr_dn,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1139,APR_Hepat_CellLoss_1hr_dn,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1139,APR_Hepat_CellLoss_1hr_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1139,APR_Hepat_CellLoss_1hr_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1139,APR_Hepat_CellLoss_1hr_dn,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1139,APR_Hepat_CellLoss_1hr_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1139,APR_Hepat_CellLoss_1hr_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1139,APR_Hepat_CellLoss_1hr_dn,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1140,APR_Hepat_CellLoss_1hr_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1140,APR_Hepat_CellLoss_1hr_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1140,APR_Hepat_CellLoss_1hr_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1140,APR_Hepat_CellLoss_1hr_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -1140,APR_Hepat_CellLoss_1hr_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -1140,APR_Hepat_CellLoss_1hr_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -1140,APR_Hepat_CellLoss_1hr_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -1140,APR_Hepat_CellLoss_1hr_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1147,APR_Hepat_MitoFxnI_1hr_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1147,APR_Hepat_MitoFxnI_1hr_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1147,APR_Hepat_MitoFxnI_1hr_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1147,APR_Hepat_MitoFxnI_1hr_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1147,APR_Hepat_MitoFxnI_1hr_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1148,APR_Hepat_MitoFxnI_1hr_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1148,APR_Hepat_MitoFxnI_1hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1148,APR_Hepat_MitoFxnI_1hr_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1148,APR_Hepat_MitoFxnI_1hr_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1148,APR_Hepat_MitoFxnI_1hr_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1148,APR_Hepat_MitoFxnI_1hr_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1148,APR_Hepat_MitoFxnI_1hr_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1148,APR_Hepat_MitoFxnI_1hr_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1148,APR_Hepat_MitoFxnI_1hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1148,APR_Hepat_MitoFxnI_1hr_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1148,APR_Hepat_MitoFxnI_1hr_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1153,APR_Hepat_Apoptosis_24hr_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -1153,APR_Hepat_Apoptosis_24hr_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -1153,APR_Hepat_Apoptosis_24hr_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -1153,APR_Hepat_Apoptosis_24hr_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1153,APR_Hepat_Apoptosis_24hr_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -1153,APR_Hepat_Apoptosis_24hr_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1153,APR_Hepat_Apoptosis_24hr_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -1153,APR_Hepat_Apoptosis_24hr_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1155,APR_Hepat_CellLoss_24hr_dn,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1155,APR_Hepat_CellLoss_24hr_dn,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1155,APR_Hepat_CellLoss_24hr_dn,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1155,APR_Hepat_CellLoss_24hr_dn,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1155,APR_Hepat_CellLoss_24hr_dn,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1155,APR_Hepat_CellLoss_24hr_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1155,APR_Hepat_CellLoss_24hr_dn,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1155,APR_Hepat_CellLoss_24hr_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1155,APR_Hepat_CellLoss_24hr_dn,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1155,APR_Hepat_CellLoss_24hr_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1155,APR_Hepat_CellLoss_24hr_dn,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1155,APR_Hepat_CellLoss_24hr_dn,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1155,APR_Hepat_CellLoss_24hr_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1156,APR_Hepat_CellLoss_24hr_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -1156,APR_Hepat_CellLoss_24hr_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -1156,APR_Hepat_CellLoss_24hr_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -1156,APR_Hepat_CellLoss_24hr_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1156,APR_Hepat_CellLoss_24hr_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1156,APR_Hepat_CellLoss_24hr_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1156,APR_Hepat_CellLoss_24hr_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -1156,APR_Hepat_CellLoss_24hr_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1163,APR_Hepat_MitoFxnI_24hr_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1163,APR_Hepat_MitoFxnI_24hr_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1163,APR_Hepat_MitoFxnI_24hr_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1163,APR_Hepat_MitoFxnI_24hr_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1163,APR_Hepat_MitoFxnI_24hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1164,APR_Hepat_MitoFxnI_24hr_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1164,APR_Hepat_MitoFxnI_24hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1164,APR_Hepat_MitoFxnI_24hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1164,APR_Hepat_MitoFxnI_24hr_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1164,APR_Hepat_MitoFxnI_24hr_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1164,APR_Hepat_MitoFxnI_24hr_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1164,APR_Hepat_MitoFxnI_24hr_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1164,APR_Hepat_MitoFxnI_24hr_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1164,APR_Hepat_MitoFxnI_24hr_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1164,APR_Hepat_MitoFxnI_24hr_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1164,APR_Hepat_MitoFxnI_24hr_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1169,APR_Hepat_Apoptosis_48hr_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -1169,APR_Hepat_Apoptosis_48hr_dn,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -1169,APR_Hepat_Apoptosis_48hr_dn,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1169,APR_Hepat_Apoptosis_48hr_dn,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -1169,APR_Hepat_Apoptosis_48hr_dn,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -1169,APR_Hepat_Apoptosis_48hr_dn,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1169,APR_Hepat_Apoptosis_48hr_dn,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -1169,APR_Hepat_Apoptosis_48hr_dn,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1171,APR_Hepat_CellLoss_48hr_dn,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1171,APR_Hepat_CellLoss_48hr_dn,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1171,APR_Hepat_CellLoss_48hr_dn,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1171,APR_Hepat_CellLoss_48hr_dn,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1171,APR_Hepat_CellLoss_48hr_dn,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1171,APR_Hepat_CellLoss_48hr_dn,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1171,APR_Hepat_CellLoss_48hr_dn,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1171,APR_Hepat_CellLoss_48hr_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1171,APR_Hepat_CellLoss_48hr_dn,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1171,APR_Hepat_CellLoss_48hr_dn,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1171,APR_Hepat_CellLoss_48hr_dn,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1171,APR_Hepat_CellLoss_48hr_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1171,APR_Hepat_CellLoss_48hr_dn,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1172,APR_Hepat_CellLoss_48hr_up,200,Estrogen receptor activation leading to breast cancer,1182,"Increase, Cell Proliferation (Epithelial Cells)",KE -1172,APR_Hepat_CellLoss_48hr_up,37,PPARalpha-dependent liver cancer,716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1172,APR_Hepat_CellLoss_48hr_up,117,androgen receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1172,APR_Hepat_CellLoss_48hr_up,107,Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas (in mouse and rat),716,"Increase, Mitogenic cell proliferation (hepatocytes)",KE -1172,APR_Hepat_CellLoss_48hr_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,142,"Hyperplasia, Hyperplasia",KE -1172,APR_Hepat_CellLoss_48hr_up,171,Chronic cytotoxicity of the serous membrane leading to pleural/peritoneal mesotheliomas in the rat.,1089,"Increased, Cell Proliferation (mesothelium)",KE -1172,APR_Hepat_CellLoss_48hr_up,1,Uncharacterized liver damage leading to hepatocellular carcinoma,57,"Proliferation, Cell proliferation in the absence of cytotoxicity",KE -1172,APR_Hepat_CellLoss_48hr_up,200,Estrogen receptor activation leading to breast cancer,1189,"Increased, Proliferation (Endothelial cells)",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1179,APR_Hepat_MitoFxnI_48hr_dn,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1179,APR_Hepat_MitoFxnI_48hr_dn,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1179,APR_Hepat_MitoFxnI_48hr_dn,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1179,APR_Hepat_MitoFxnI_48hr_dn,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1179,APR_Hepat_MitoFxnI_48hr_dn,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1180,APR_Hepat_MitoFxnI_48hr_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1180,APR_Hepat_MitoFxnI_48hr_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1180,APR_Hepat_MitoFxnI_48hr_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1180,APR_Hepat_MitoFxnI_48hr_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1180,APR_Hepat_MitoFxnI_48hr_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1180,APR_Hepat_MitoFxnI_48hr_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1180,APR_Hepat_MitoFxnI_48hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1180,APR_Hepat_MitoFxnI_48hr_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1180,APR_Hepat_MitoFxnI_48hr_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1180,APR_Hepat_MitoFxnI_48hr_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1180,APR_Hepat_MitoFxnI_48hr_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1185,TOX21_ARE_BLA_agonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1185,TOX21_ARE_BLA_agonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1185,TOX21_ARE_BLA_agonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1185,TOX21_ARE_BLA_agonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1185,TOX21_ARE_BLA_agonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1185,TOX21_ARE_BLA_agonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1185,TOX21_ARE_BLA_agonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1185,TOX21_ARE_BLA_agonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1185,TOX21_ARE_BLA_agonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1185,TOX21_ARE_BLA_agonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1185,TOX21_ARE_BLA_agonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1185,TOX21_ARE_BLA_agonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1185,TOX21_ARE_BLA_agonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1186,TOX21_HSE_BLA_agonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1186,TOX21_HSE_BLA_agonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1186,TOX21_HSE_BLA_agonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1186,TOX21_HSE_BLA_agonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1186,TOX21_HSE_BLA_agonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1186,TOX21_HSE_BLA_agonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1186,TOX21_HSE_BLA_agonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1186,TOX21_HSE_BLA_agonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1186,TOX21_HSE_BLA_agonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1186,TOX21_HSE_BLA_agonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1186,TOX21_HSE_BLA_agonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1186,TOX21_HSE_BLA_agonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1186,TOX21_HSE_BLA_agonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1187,TOX21_p53_BLA_p1_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1187,TOX21_p53_BLA_p1_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1187,TOX21_p53_BLA_p1_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1187,TOX21_p53_BLA_p1_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1187,TOX21_p53_BLA_p1_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1187,TOX21_p53_BLA_p1_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1187,TOX21_p53_BLA_p1_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1187,TOX21_p53_BLA_p1_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1187,TOX21_p53_BLA_p1_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1187,TOX21_p53_BLA_p1_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1187,TOX21_p53_BLA_p1_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1187,TOX21_p53_BLA_p1_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1187,TOX21_p53_BLA_p1_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1188,TOX21_FXR_BLA_agonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1188,TOX21_FXR_BLA_agonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1188,TOX21_FXR_BLA_agonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1188,TOX21_FXR_BLA_agonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1188,TOX21_FXR_BLA_agonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1188,TOX21_FXR_BLA_agonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1188,TOX21_FXR_BLA_agonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1188,TOX21_FXR_BLA_agonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1188,TOX21_FXR_BLA_agonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1188,TOX21_FXR_BLA_agonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1188,TOX21_FXR_BLA_agonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1188,TOX21_FXR_BLA_agonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1188,TOX21_FXR_BLA_agonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1195,TOX21_PPARd_BLA_Agonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1195,TOX21_PPARd_BLA_Agonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1195,TOX21_PPARd_BLA_Agonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1195,TOX21_PPARd_BLA_Agonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1195,TOX21_PPARd_BLA_Agonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1195,TOX21_PPARd_BLA_Agonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1195,TOX21_PPARd_BLA_Agonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1195,TOX21_PPARd_BLA_Agonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1195,TOX21_PPARd_BLA_Agonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1195,TOX21_PPARd_BLA_Agonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1195,TOX21_PPARd_BLA_Agonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1195,TOX21_PPARd_BLA_Agonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1195,TOX21_PPARd_BLA_Agonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1204,ATG_XTT_Cytotoxicity_up,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1204,ATG_XTT_Cytotoxicity_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1204,ATG_XTT_Cytotoxicity_up,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1204,ATG_XTT_Cytotoxicity_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1204,ATG_XTT_Cytotoxicity_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1204,ATG_XTT_Cytotoxicity_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1204,ATG_XTT_Cytotoxicity_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1204,ATG_XTT_Cytotoxicity_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1204,ATG_XTT_Cytotoxicity_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1204,ATG_XTT_Cytotoxicity_up,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1204,ATG_XTT_Cytotoxicity_up,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1204,ATG_XTT_Cytotoxicity_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1204,ATG_XTT_Cytotoxicity_up,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1204,ATG_XTT_Cytotoxicity_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1204,ATG_XTT_Cytotoxicity_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1204,ATG_XTT_Cytotoxicity_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1204,ATG_XTT_Cytotoxicity_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1204,ATG_XTT_Cytotoxicity_up,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1204,ATG_XTT_Cytotoxicity_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1204,ATG_XTT_Cytotoxicity_up,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1204,ATG_XTT_Cytotoxicity_up,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1204,ATG_XTT_Cytotoxicity_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1204,ATG_XTT_Cytotoxicity_up,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1204,ATG_XTT_Cytotoxicity_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1318,TOX21_p53_BLA_p2_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1318,TOX21_p53_BLA_p2_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1318,TOX21_p53_BLA_p2_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1318,TOX21_p53_BLA_p2_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1318,TOX21_p53_BLA_p2_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1318,TOX21_p53_BLA_p2_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1318,TOX21_p53_BLA_p2_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1318,TOX21_p53_BLA_p2_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1318,TOX21_p53_BLA_p2_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1318,TOX21_p53_BLA_p2_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1318,TOX21_p53_BLA_p2_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1318,TOX21_p53_BLA_p2_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1318,TOX21_p53_BLA_p2_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1322,TOX21_p53_BLA_p3_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1322,TOX21_p53_BLA_p3_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1322,TOX21_p53_BLA_p3_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1322,TOX21_p53_BLA_p3_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1322,TOX21_p53_BLA_p3_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1322,TOX21_p53_BLA_p3_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1322,TOX21_p53_BLA_p3_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1322,TOX21_p53_BLA_p3_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1322,TOX21_p53_BLA_p3_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1322,TOX21_p53_BLA_p3_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1322,TOX21_p53_BLA_p3_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1322,TOX21_p53_BLA_p3_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1322,TOX21_p53_BLA_p3_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1326,TOX21_p53_BLA_p4_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1326,TOX21_p53_BLA_p4_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1326,TOX21_p53_BLA_p4_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1326,TOX21_p53_BLA_p4_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1326,TOX21_p53_BLA_p4_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1326,TOX21_p53_BLA_p4_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1326,TOX21_p53_BLA_p4_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1330,TOX21_p53_BLA_p5_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1330,TOX21_p53_BLA_p5_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1330,TOX21_p53_BLA_p5_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1330,TOX21_p53_BLA_p5_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1330,TOX21_p53_BLA_p5_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1330,TOX21_p53_BLA_p5_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1330,TOX21_p53_BLA_p5_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1330,TOX21_p53_BLA_p5_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1330,TOX21_p53_BLA_p5_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1330,TOX21_p53_BLA_p5_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1330,TOX21_p53_BLA_p5_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1330,TOX21_p53_BLA_p5_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1330,TOX21_p53_BLA_p5_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1331,TOX21_VDR_BLA_Agonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1331,TOX21_VDR_BLA_Agonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1331,TOX21_VDR_BLA_Agonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1331,TOX21_VDR_BLA_Agonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1331,TOX21_VDR_BLA_Agonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1331,TOX21_VDR_BLA_Agonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1331,TOX21_VDR_BLA_Agonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1343,TOX21_ESRE_BLA_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1343,TOX21_ESRE_BLA_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1343,TOX21_ESRE_BLA_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1343,TOX21_ESRE_BLA_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1343,TOX21_ESRE_BLA_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1343,TOX21_ESRE_BLA_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1343,TOX21_ESRE_BLA_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1343,TOX21_ESRE_BLA_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1343,TOX21_ESRE_BLA_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1343,TOX21_ESRE_BLA_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1343,TOX21_ESRE_BLA_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1343,TOX21_ESRE_BLA_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1343,TOX21_ESRE_BLA_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1346,TOX21_NFkB_BLA_agonist_ratio,14,Glucocorticoid receptor activation leading to increased disease susceptibility,202,"Inhibition, Nuclear factor kappa B (NF-kB)",KE -1347,TOX21_NFkB_BLA_agonist_viability,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1347,TOX21_NFkB_BLA_agonist_viability,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1347,TOX21_NFkB_BLA_agonist_viability,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1347,TOX21_NFkB_BLA_agonist_viability,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1347,TOX21_NFkB_BLA_agonist_viability,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1347,TOX21_NFkB_BLA_agonist_viability,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1347,TOX21_NFkB_BLA_agonist_viability,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1347,TOX21_NFkB_BLA_agonist_viability,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1347,TOX21_NFkB_BLA_agonist_viability,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1347,TOX21_NFkB_BLA_agonist_viability,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1347,TOX21_NFkB_BLA_agonist_viability,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1347,TOX21_NFkB_BLA_agonist_viability,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1347,TOX21_NFkB_BLA_agonist_viability,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1350,ATG_COUP_TF2_TRANS2_up,74,Modulation of adult leydig cell function subsequent to hypermethylation in the fetal testis,655,"Decreased testosterone by the fetal Leydig cells, Increased COUP-TFII in fetal Leydig cells",KE -1350,ATG_COUP_TF2_TRANS2_up,67,Modulation of adult leydig cell function subsequent estradiol activation in the fetal testis,655,"Decreased testosterone by the fetal Leydig cells, Increased COUP-TFII in fetal Leydig cells",KE -1350,ATG_COUP_TF2_TRANS2_up,66,Modulation of adult leydig cell function subsequent glucocorticoid activation in the fetal testis,655,"Decreased testosterone by the fetal Leydig cells, Increased COUP-TFII in fetal Leydig cells",KE -1350,ATG_COUP_TF2_TRANS2_up,68,Modulation of adult leydig cell function subsequent to alterations in the fetal testis protome,655,"Decreased testosterone by the fetal Leydig cells, Increased COUP-TFII in fetal Leydig cells",KE -1372,Tanguay_ZF_120hpf_MORT_up,4,Ecdysone receptor (EcR) activation leading to mortality in Daphnia magna,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,16,Acetylcholinesterase inhibition leading to acute mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,99,histamine (H2) receptor antagonism leading to reduced survival,636,"Decreased, survival",AO -1372,Tanguay_ZF_120hpf_MORT_up,21,AhR activation leading to early life stage mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,96,axonal sodium channel modulation leading to acute mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,636,"Decreased, survival",AO -1372,Tanguay_ZF_120hpf_MORT_up,95,Ether-a-go-go (ERG) voltage-gated potassium channel inhibition leading to reduced survival,592,"Reduced, survival",AO -1372,Tanguay_ZF_120hpf_MORT_up,104,altered ion channel activity leading to impaired heart function,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,91,Sodium channel inhibition leading to reduced survival,592,"Reduced, survival",AO -1372,Tanguay_ZF_120hpf_MORT_up,161,Glutamate-gated chloride channel activation leading to neurotransmission inhibition associated mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,155,Deiodinase 2 inhibition leading to reduced young of year survival via posterior swim bladder inflation,1006,"Reduced, Young of year survival",KE -1372,Tanguay_ZF_120hpf_MORT_up,158,Deiodinase 1 inhibition leading to reduced young of year survival via anterior swim bladder inflaction,1006,"Reduced, Young of year survival",KE -1372,Tanguay_ZF_120hpf_MORT_up,159,thyroperoxidase inhibition leading to reduced young of year survival via anterior swim bladder inflation,1006,"Reduced, Young of year survival",KE -1372,Tanguay_ZF_120hpf_MORT_up,113,Glutamate-gated chloride channel activation leading to acute mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,156,Deiodinase 2 inhibition leading to reduced young of year survival via anterior swim bladder inflation,1006,"Reduced, Young of year survival",KE -1372,Tanguay_ZF_120hpf_MORT_up,177,Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,160,Ionotropic gamma-aminbutyric acid receptor activation mediated neurotransmissino inhibition leading to mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,351,"Increased, Mortality",AO -1372,Tanguay_ZF_120hpf_MORT_up,157,Deiodinase 1 inhibition leading to reduced young of year survival via posterior swim bladder inflaction,1006,"Reduced, Young of year survival",KE -1372,Tanguay_ZF_120hpf_MORT_up,186,unknown MIE leading to renal failure and mortality,351,"Increased, Mortality",AO -1373,Tanguay_ZF_120hpf_YSE_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1373,Tanguay_ZF_120hpf_YSE_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1374,Tanguay_ZF_120hpf_AXIS_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1374,Tanguay_ZF_120hpf_AXIS_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1374,Tanguay_ZF_120hpf_AXIS_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1466,Notochord distortion or malformations,KE -1375,Tanguay_ZF_120hpf_EYE_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1376,Tanguay_ZF_120hpf_SNOU_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1377,Tanguay_ZF_120hpf_JAW_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1378,Tanguay_ZF_120hpf_OTIC_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1378,Tanguay_ZF_120hpf_OTIC_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1379,Tanguay_ZF_120hpf_PE_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1379,Tanguay_ZF_120hpf_PE_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1380,Tanguay_ZF_120hpf_BRAI_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1380,Tanguay_ZF_120hpf_BRAI_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1381,Tanguay_ZF_120hpf_SOMI_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1382,Tanguay_ZF_120hpf_PFIN_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1382,Tanguay_ZF_120hpf_PFIN_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1383,Tanguay_ZF_120hpf_CFIN_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1383,Tanguay_ZF_120hpf_CFIN_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1385,Tanguay_ZF_120hpf_CIRC_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1386,Tanguay_ZF_120hpf_TRUN_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1386,Tanguay_ZF_120hpf_TRUN_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1466,Notochord distortion or malformations,KE -1386,Tanguay_ZF_120hpf_TRUN_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1467,"Growth, reduction",AO -1386,Tanguay_ZF_120hpf_TRUN_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1387,Tanguay_ZF_120hpf_SWIM_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1387,Tanguay_ZF_120hpf_SWIM_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1388,Tanguay_ZF_120hpf_NC_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1388,Tanguay_ZF_120hpf_NC_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1466,Notochord distortion or malformations,KE -1388,Tanguay_ZF_120hpf_NC_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1389,Tanguay_ZF_120hpf_TR_up,16,Acetylcholinesterase inhibition leading to acute mortality,39,"Induction, Ataxia, paralysis, or hyperactivity",KE -1389,Tanguay_ZF_120hpf_TR_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1467,"Growth, reduction",AO -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1466,Notochord distortion or malformations,KE -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,157,Deiodinase 1 inhibition leading to reduced young of year survival via posterior swim bladder inflaction,1005,"Reduced, Swimming performance",KE -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,43,VEGF Signaling and vascular disruption leading to adverse developmental outcomes,1001,"Increased, Developmental Defects",AO -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,29,Estrogen receptor agonism leading to reproductive dysfunction,339,"Altered, Larval development",AO -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,156,Deiodinase 2 inhibition leading to reduced young of year survival via anterior swim bladder inflation,1005,"Reduced, Swimming performance",KE -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,158,Deiodinase 1 inhibition leading to reduced young of year survival via anterior swim bladder inflaction,1005,"Reduced, Swimming performance",KE -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,159,thyroperoxidase inhibition leading to reduced young of year survival via anterior swim bladder inflation,1005,"Reduced, Swimming performance",KE -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1005,"Reduced, Swimming performance",KE -1507,NHEERL_ZF_144hpf_TERATOSCORE_up,155,Deiodinase 2 inhibition leading to reduced young of year survival via posterior swim bladder inflation,1005,"Reduced, Swimming performance",KE -1509,NCCT_HEK293T_CellTiterGLO,144,Lysosomal damage leading to liver inflammation,55,"N/A, Cell injury/death",KE -1509,NCCT_HEK293T_CellTiterGLO,12,Binding of antagonist to NMDARs during brain development can trigger neuroinflammation and lead to neurodegeneration,55,"N/A, Cell injury/death",KE -1509,NCCT_HEK293T_CellTiterGLO,13,binding of antagonist to NMDARs during brain development induces impairment of learnaing and memory abilities,55,"N/A, Cell injury/death",KE -1509,NCCT_HEK293T_CellTiterGLO,38,protein alkylation leading to liver fibrosis,55,"N/A, Cell injury/death",KE -1509,NCCT_HEK293T_CellTiterGLO,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",55,"N/A, Cell injury/death",KE -1509,NCCT_HEK293T_CellTiterGLO,205,AoP from chemical insult to cell death,1262,Apoptosis,AO -1509,NCCT_HEK293T_CellTiterGLO,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,1262,Apoptosis,KE -1509,NCCT_HEK293T_CellTiterGLO,212,Histone Deacetylase Inhibition Leading to Testicular Toxicity,1262,Apoptosis,KE -1509,NCCT_HEK293T_CellTiterGLO,205,AoP from chemical insult to cell death,1263,Necrosis,AO -1509,NCCT_HEK293T_CellTiterGLO,209,Perturbation of cholesterol and glutathione homeostasis leading to hepatotoxicity: Integrated multi-OMICS approach for building AOP,1291,Hepatotoxicity,AO -1509,NCCT_HEK293T_CellTiterGLO,216,Excessive reactive oxygen species production leading to reproductive dysfunction,1365,"Increase, Apoptosis",KE -1509,NCCT_HEK293T_CellTiterGLO,17,binding to SH/selen-proteins can trigger neuroinflammation leading to neurodegeneration,55,"N/A, Cell injury/death",KE -1509,NCCT_HEK293T_CellTiterGLO,35,Narcosis leading to respiratory failure,169,"Disruption, Membrane integrity",KE -1623,CLD_HMGCS2_6hr,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1623,CLD_HMGCS2_6hr,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1623,CLD_HMGCS2_6hr,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1623,CLD_HMGCS2_6hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1623,CLD_HMGCS2_6hr,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1623,CLD_HMGCS2_6hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1623,CLD_HMGCS2_6hr,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1623,CLD_HMGCS2_6hr,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1623,CLD_HMGCS2_6hr,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1623,CLD_HMGCS2_6hr,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1623,CLD_HMGCS2_6hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1639,CLD_HMGCS2_24hr,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1639,CLD_HMGCS2_24hr,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1639,CLD_HMGCS2_24hr,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1639,CLD_HMGCS2_24hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1639,CLD_HMGCS2_24hr,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1639,CLD_HMGCS2_24hr,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1639,CLD_HMGCS2_24hr,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1639,CLD_HMGCS2_24hr,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1639,CLD_HMGCS2_24hr,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1639,CLD_HMGCS2_24hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1639,CLD_HMGCS2_24hr,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1639,CLD_HMGCS2_24hr,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1655,CLD_HMGCS2_48hr,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1655,CLD_HMGCS2_48hr,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1655,CLD_HMGCS2_48hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1655,CLD_HMGCS2_48hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1655,CLD_HMGCS2_48hr,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1655,CLD_HMGCS2_48hr,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1655,CLD_HMGCS2_48hr,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1655,CLD_HMGCS2_48hr,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1655,CLD_HMGCS2_48hr,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1655,CLD_HMGCS2_48hr,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1797,Tanguay_ZF_120hpf_ActivityScore,159,thyroperoxidase inhibition leading to reduced young of year survival via anterior swim bladder inflation,1005,"Reduced, Swimming performance",KE -1797,Tanguay_ZF_120hpf_ActivityScore,242,inhibition of lysyl oxidase leading to enhanced chronic fish toxicity,1005,"Reduced, Swimming performance",KE -1797,Tanguay_ZF_120hpf_ActivityScore,158,Deiodinase 1 inhibition leading to reduced young of year survival via anterior swim bladder inflaction,1005,"Reduced, Swimming performance",KE -1797,Tanguay_ZF_120hpf_ActivityScore,16,Acetylcholinesterase inhibition leading to acute mortality,39,"Induction, Ataxia, paralysis, or hyperactivity",KE -1797,Tanguay_ZF_120hpf_ActivityScore,156,Deiodinase 2 inhibition leading to reduced young of year survival via anterior swim bladder inflation,1005,"Reduced, Swimming performance",KE -1797,Tanguay_ZF_120hpf_ActivityScore,99,histamine (H2) receptor antagonism leading to reduced survival,636,"Decreased, survival",AO -1797,Tanguay_ZF_120hpf_ActivityScore,157,Deiodinase 1 inhibition leading to reduced young of year survival via posterior swim bladder inflaction,1005,"Reduced, Swimming performance",KE -1797,Tanguay_ZF_120hpf_ActivityScore,138,Organic anion transporter (OAT1) inhibition leadin to renal failure and mortality,351,"Increased, Mortality",AO -1797,Tanguay_ZF_120hpf_ActivityScore,99,histamine (H2) receptor antagonism leading to reduced survival,635,"Decreased, locomotion",KE -1797,Tanguay_ZF_120hpf_ActivityScore,155,Deiodinase 2 inhibition leading to reduced young of year survival via posterior swim bladder inflation,1005,"Reduced, Swimming performance",KE -1824,NCCT_TPO_GUA_dn,175,Thyroperoxidase inhibition leading to altered amphibian metamorphosis,279,"Thyroperoxidase, Inhibition",MIE -1824,NCCT_TPO_GUA_dn,42,inhibition of thyroperoxidase and subsequent adverse neurodevelopmental outcomes in mammals,279,"Thyroperoxidase, Inhibition",MIE -1824,NCCT_TPO_GUA_dn,159,thyroperoxidase inhibition leading to reduced young of year survival via anterior swim bladder inflation,279,"Thyroperoxidase, Inhibition",MIE -1824,NCCT_TPO_GUA_dn,119,Inhibition of thyroid peroxidase leading to follicular cell adenomas and carcinomas (in rat and mouse),279,"Thyroperoxidase, Inhibition",MIE -1854,TOX21_MMP_ratio_up,207,NADPH oxidase and p38 MAPK activation leading to reproductive failure,176,"Damaging, Mitochondria",KE -1854,TOX21_MMP_ratio_up,34,LXR activation to liver steatosis,176,"Damaging, Mitochondria",KE -1854,TOX21_MMP_ratio_up,200,Estrogen receptor activation leading to breast cancer,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,78,Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,888,"Binding of inhibitor, NADH-ubiquinone oxidoreductase (complex I)",MIE -1854,TOX21_MMP_ratio_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,887,"Inhibition, NADH-ubiquinone oxidoreductase (complex I)",KE -1854,TOX21_MMP_ratio_up,130,Phospholipase A inhibitors lead to hepatotoxicity,832,"Injury, Mitochondria",KE -1854,TOX21_MMP_ratio_up,26,Calcium-mediated neuronal ROS production and energy imbalance,178,"Disruption, Mitochondrial electron transport chain",KE -1854,TOX21_MMP_ratio_up,178,Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,87,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,144,Lysosomal damage leading to liver inflammation,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,79,Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,238,Excessive reactive oxygen species production leading to reproductive failure,1446,"Increase, Uncoupling of oxidative phosphorylation",KE -1854,TOX21_MMP_ratio_up,77,Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,48,"binding of agonists to N-methyl-D-aspartate receptor (NMDAR) in adult brain causes exicitoxicity that mediates neuronal cell death, contributing to learning and memory impairment",177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,3,Inhibition of the mitochondrial complex I of nigra-striatal neurons leads to parkinsonian motor deficits,177,"N/A, Mitochondrial dysfunction 1",KE -1854,TOX21_MMP_ratio_up,187,Anticoagulant rodenticide inhibition of vitamin K epoxide reductase resulting coagulopathy and hemorrhage,1138,"Uncoupling of oxidative phosphorylation, Reduced ability to generate ATP",MIE -1854,TOX21_MMP_ratio_up,205,AoP from chemical insult to cell death,1260,Direct mitochondrial inhibition,MIE -1854,TOX21_MMP_ratio_up,205,AoP from chemical insult to cell death,1261,Mitochondrial impairment,KE -1854,TOX21_MMP_ratio_up,80,Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure,177,"N/A, Mitochondrial dysfunction 1",KE diff --git a/inst/extdata/OWC_data_fromSup.xlsx b/inst/extdata/OWC_data_fromSup.xlsx index 9b62f0d1..43db7b77 100644 Binary files a/inst/extdata/OWC_data_fromSup.xlsx and b/inst/extdata/OWC_data_fromSup.xlsx differ diff --git a/inst/extdata/landuse.csv b/inst/extdata/landuse.csv deleted file mode 100644 index d8fadc98..00000000 --- a/inst/extdata/landuse.csv +++ /dev/null @@ -1,58 +0,0 @@ -site,DA,Mean annual streamflow (cfs),Population,Population Density,Urban %,"Ag: pasture, hay %",Ag: crops %,Ag: total %,Forest %,"Water, wetland %",Other land use %,Impervious %,,Number of WWTPs,WWTP flow (MGD),WWTP flow as fraction of river discharge,WWTP flow as fraction of river discharge- distance weighted,WWTP population served,WWTP population served- distance weighted -"04024000",3430,2418,81474,23.7,2.9,2.6,0.3,2.9,33.6,50.7,10,0.5,Wetland,17,8.24,0.00527,3.23E-05,50507,319 -"04024430",420,454,6185,14.6,3,9,0.9,9.9,48.5,28.6,9.9,0.2,Wetland,1,NA,NA,NA,NA,NA -"04027000",597,624,4317,7.2,3.4,3.7,2.1,5.8,71.1,15.8,3.9,0.3,Forest,4,0.18,0.00044,9.56E-06,1240,28 -"04027500",301,268,1774,6.6,3.6,4.6,0.9,5.5,68.1,16.4,6.4,0.3,Forest,3,0.03,0.00019,6.26E-06,72,3 -"04029990",262,300,10770,40.2,5.5,0,4.3,4.3,58.4,28,3.9,0.8,Wetland,2,2.02,0.01039,0.000327674,8862,281 -"04032000",261,91,2093,7.9,3.2,0,0,0,58.4,37.3,1.1,0.2,Wetland,2,0.03,0.00051,1.65E-05,NA,NA -"04040000",1340,1067,4490,3.4,2.5,1.9,1.6,3.5,68.8,21.9,3.3,0.2,Forest,6,0.19,0.00027,7.14E-06,1075,16 -"04043000",705,791*,2918,4.1,2.2,2.9,1,3.9,69.6,20.4,3.8,0.3,Forest,1,0.08,0.00015,2.86E-06,838,16 -"04045500",790,817,6030,8,2.4,0.5,0.2,0.8,33.9,57.9,5,0.4,Wetland,2,1.45,0.00275,5.31E-05,2000,39 -"04056500",1100,1278,3754,3.3,2.4,0.6,0.3,1,34.7,54.1,7.8,0.3,Wetland,2,0.02,0.00002,2.31E-07,180,2 -"04059000",870,559,11792,13.5,3.5,1.4,0.4,1.7,45.4,38.3,11.1,0.6,Wetland,2,0.26,0.00071,1.23E-05,3550,63 -"04059500",450,263,2149,4.8,2.8,2.8,0.8,3.6,40.5,47.2,6,0.3,Wetland,1,0.02,0.00013,9.40E-07,NA,NA -"04067500",3930,2618,57816,14.8,3.8,1.4,2.3,3.7,54.8,33.1,4.6,0.5,Wetland,12,3.13,0.00185,1.98E-05,28801,267 -"04069500",1080,756,20964,18.9,4.4,4,11.4,15.5,43,34,3.1,0.5,Wetland,5,0.52,0.00107,1.43E-05,4960,49 -"04071765",966,692,21397,22.7,4.9,6.1,15.1,21.2,41.4,30.5,2,0.5,Crops,7,0.87,0.00196,9.18E-05,6595,265 -"040851385",6330,5270,795673,125,8.4,9.7,31.7,41.4,24.7,24.4,1.1,2.2,Crops,86,83.78,0.02461,0.000582079,485082,27140 -"04085427",526,430,32918,63.5,7,31.8,38.1,69.9,6.2,15.5,1.4,1.6,AgMix,12,2.42,0.00872,0.000145436,15887,268 -"04087170",872,780,971085,1123,29.9,15.7,27.5,43.2,12,13.2,1.7,11.8,Urban,13,11.17,0.02217,0.000361233,17047,234 -"04092750",39,500,90992,2363,84.1,0.9,0.1,1,3.3,8.7,2.9,47.3,Urban,4,151.57,0.46927,0.051020684,NA,NA -"04095090",331,553,296105,895,38,6.1,21.3,27.4,13.1,10.4,11.2,14.1,Urban,15,20.1,0.05629,0.007992122,64700,2070 -"04101500",3666,3535,770579,207,13.8,12.9,47.5,60.4,9.8,14.8,1.2,3.8,Crops,60,160.9,0.07047,0.001725198,117953,837 -"04102500",390,486,61162,157,11,7.3,40.8,48.1,21.1,16.8,3,2.4,Crops,5,1.83,0.00582,0.000515662,19250,1647 -"04108660",1950,2186,466585,236,13.6,15.4,33.2,48.7,21,14.7,2,3.6,AgMix,24,47.39,0.03355,0.000324439,54642,459 -"04119400",5290,5195,1492566,282,14.8,17.2,36.8,54,16.2,13.6,1.4,4.3,AgMix,77,160.35,0.04778,0.000735832,609173,6845 -"04121970",2313,2101,132948,57.1,7.6,7.9,11.6,19.5,39.4,23.1,10.3,1.4,Forest,9,5.88,0.00433,3.30E-05,45646,407 -"04122200",406,441,16900,41.7,5.3,4.3,16,20.3,49.2,15,10.1,0.9,Crops,1,NA,NA,NA,NA,NA -"04122500",681,733,15031,21.8,5,2.1,7.3,9.4,61,15.7,8.9,0.6,Forest,2,0.08,0.00017,1.07E-05,300,32 -"04126010",1730,1725,45410,26.5,5.8,1.9,7.7,9.7,54.2,13.4,17,0.8,Forest,1,NA,NA,NA,NA,NA -"04128500",598,537*,21594,37.2,6.8,1.2,7,8.3,51.7,21,12.4,1.1,Forest,1,NA,NA,NA,NA,NA -"04132052",1500,784*,39554,26.5,6.2,1.5,5.3,6.8,47.9,26,13.2,0.9,Wetland,1,0.06,0.00012,2.41E-06,900,18 -"04135020",1240,773*,23261,18.6,5.9,5.5,5,10.5,42.7,32,8.9,0.9,Wetland,1,1.06,0.00211,3.13E-05,685,10 -"04137500",1739,1277,49658,28.5,8.5,0.7,1.4,2.1,57.4,13.5,18.5,1.3,Forest,1,0.12,0.00015,7.80E-07,1200,6 -"04142000",320,351,17781,53.4,9.2,10,6.8,16.8,43.1,20.9,10,1.7,Forest,2,3.72,0.01638,0.000267352,653,10 -"04157005",6060,5186,1077130,180,12.2,13.9,30.5,44.5,23.4,15.6,4.4,3,Crops,68,98.62,0.02944,0.00049525,333551,4205 -"04159492",464,325,23745,51.3,6.4,14.4,60.7,75.2,9.1,8,1.3,1.3,Crops,7,6.05,0.02885,0.000505537,10207,257 -"04165500",734,730,1183078,1582,52.8,7.6,11.9,19.5,14.5,11.2,2,20.7,Urban,10,30.32,0.06428,0.00313645,10600,227 -"04166500",187,167,459415,2498,92,0.1,0,0.1,4.5,3.1,0.3,33.6,Urban,4,NA,NA,NA,NA,NA -"04174500",729,598,382841,512,28.9,13.5,11.7,25.2,22.7,21.1,2.1,7,Urban,14,9.49,0.02457,0.000443549,80888,1438 -"04176500",1042,895,158759,153,11.1,18,49.3,67.3,10.8,9.6,1.2,2.6,AgMix,25,10.78,0.01864,0.000316332,56276,698 -"04193500",6330,6421,882975,140,10.7,5.5,73.3,78.7,6.4,3,1.1,2.6,Crops,192,129.73,0.03128,0.000506555,468777,12838 -"04195500",428,546,49406,114,9.8,0.6,83.6,84.2,4.5,0.7,0.8,2.3,Crops,14,12.6,0.03572,0.000945922,41233,1396 -"04198000",1251,1834,98120,78.4,8.3,3.1,77.4,80.5,8.8,0.9,1.5,1.7,Crops,41,11.93,0.01007,0.000167,36012,391 -"04199000",371,510,45775,123,9.5,2.1,72.5,74.5,14.5,1,0.4,2.2,Crops,19,6.03,0.01829,0.002058355,10900,329 -"04199500",262,355,20323,77.5,7.5,10.2,55.4,65.6,23.2,3.1,0.6,1.2,Crops,12,1.35,0.00591,0.000100277,6069,111 -"04200500",396,558,98307,248,12.6,16.7,39.5,56.2,22.5,7.8,1,2.7,AgMix,17,3.24,0.009,0.00038517,28808,1257 -"04201500",267,487,247588,926,40.6,10,12.2,22.2,30.1,5.9,1.2,9.5,Urban,13,15.07,0.04787,0.002050535,36880,1844 -"04208000",707,1284,598774,845,40,8.6,8.9,17.5,33.1,5.7,3.7,11.2,Urban,57,94.15,0.11352,0.002994411,419483,11440 -"04212100",685,1221,87503,128,8.9,9.3,25,34.3,42.3,7.9,6.6,1.7,Crops,29,3.16,0.00401,0.01114199,15040,21287 -"04213500",436,807,28144,64.6,4.4,15.5,18.4,33.9,54.3,3.5,3.9,0.7,AgMix,4,1.26,0.00241,5.91E-05,7900,189 -"04218000",349,491,48082,138,7.3,27.2,20.7,47.9,26.5,15.4,2.9,1.5,AgMix,5,5.05,0.0159,0.000192359,31130,381 -"04231600",2474,3092,285597,116,6.6,26.3,19,45.3,37.9,6,4.2,1.3,AgMix,29,10.53,0.00527,7.01E-05,75664,1079 -"04249000",5100,7751,1039691,204,8.3,22,17.5,39.5,30.5,14.3,7.4,2,AgMix,77,130.07,0.02597,0.000363965,164081,2077 -"04260500",1864,4562,61836,33.1,1.4,7.7,3.5,11.2,67.4,11,9,0.3,Forest,20,3.74,0.00127,2.35E-05,17383,285 -"04263000",986,1902,19920,20.3,1.6,8.5,1,9.5,69.8,15.8,3.3,0.2,Forest,7,1.63,0.00133,1.99E-05,5871,118 -"04265432",598,1128,16912,28.4,1.7,7.6,2.4,10,72.4,12.7,3.1,0.2,Forest,3,1.11,0.00152,5.21E-05,7665,259 -"04268000",1125,2514,27535,24.4,1.5,1.5,0.4,1.8,78.2,15.1,3.3,0.2,Forest,7,4.89,0.00301,0.000142902,7279,204 -"04269000",612,1207,8264,13.4,1.2,1.7,0.9,2.7,75.4,16.6,4.1,0.1,Forest,2,0.15,0.00019,1.30E-05,2150,145 diff --git a/inst/shiny/heatMap.R b/inst/shiny/heatMap.R index 88d910c4..47c9ded3 100644 --- a/inst/shiny/heatMap.R +++ b/inst/shiny/heatMap.R @@ -20,6 +20,9 @@ heatMap_create <- reactive({ chem_site, category = c("Biological","Chemical","Chemical Class")[catType], plot_ND = plot_ND) + + updateAceEditor(session, editorId = "heat_out", value = heatCode() ) + return(heatMap) }) @@ -61,7 +64,7 @@ output$downloadHeatPlot_csv <- downloadHandler( } ) -output$heatCode <- renderPrint({ +heatCode <- reactive({ catType = as.numeric(input$radioMaxGroup) category <- c("Biological","Chemical","Chemical Class")[catType] diff --git a/inst/shiny/ui.R b/inst/shiny/ui.R index 71ff01da..855a343b 100644 --- a/inst/shiny/ui.R +++ b/inst/shiny/ui.R @@ -224,7 +224,7 @@ body <- dashboardBody( column(3, downloadButton('downloadHeatPlot_csv', 'Download CSV')) ), h4("R Code:"), - verbatimTextOutput("heatCode") + aceEditor(outputId = "heat_out", value = init_text, mode = "r", theme = "chrome", readOnly = TRUE) ), tags$head(tags$link(rel="shortcut icon", href="favicon.ico")) ), diff --git a/man/ACC.Rd b/man/ACC.Rd new file mode 100644 index 00000000..44bd0179 --- /dev/null +++ b/man/ACC.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/toxEval.R +\docType{data} +\name{ACC} +\alias{ACC} +\title{ACC values included with toxEval.} +\description{ +Downloaded on October 2015 from ToxCast. The data were +combined from files in the "INVITRODB_V2_LEVEL5" folder. +At the time of toxEval package release, this information was found: +\url{https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data} +in the "ToxCast & Tox21 Data Spreadsheet" data set. +} +\details{ +The data has been provided in a "wide" format, however +the \code{get_ACC} function is an easy way to get the data +in a "long" format. AC50gain values are the reported modl_ga (winning model) and units are +log micro-Molarity (log uM). +} +\examples{ +ACCColumnNames <- names(ACC) +} +\keyword{datasets} diff --git a/man/Constants.Rd b/man/Constants.Rd deleted file mode 100644 index 01b5ae31..00000000 --- a/man/Constants.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toxEval.R -\docType{data} -\name{Constants} -\alias{Constants} -\alias{ACC} -\title{Constants included with toxEval. Units are log micro-Molarity (log uM).} -\description{ -AC50gain downloaded on October 2015 from ToxCast dashboard. AC50gain values -are either the reported modl_ga (winning model) or 10% of modl_ga if the AC50gain -value is lower than the lowest measured concentration. Also, -} -\details{ -\itemize{ - \item{ACC}{ACC endpoints} -} -} -\examples{ -ACCColumnNames <- names(ACC) -} -\keyword{datasets} diff --git a/man/clean_endPoint_info.Rd b/man/clean_endPoint_info.Rd index fefd0b6a..e3eae30d 100644 --- a/man/clean_endPoint_info.Rd +++ b/man/clean_endPoint_info.Rd @@ -9,8 +9,19 @@ clean_endPoint_info(endPointInfo) \arguments{ \item{endPointInfo}{data frame Endpoint information from ToxCast} } +\value{ +data frame based on endPointInfo, but with some endPoints +filtered out, some additional categories in intended_target_family and +intended_target_family_sub. Also, the names in intended_target_family +are cleaned up to look good in graphs and tables. +} \description{ -Clean up the endPointInfo table from toxCast. Filtering and cleaning based on ES&T (cite Dan/Brett's paper) +Clean up the endPointInfo table from ToxCast. Filtering based on +\url{https://pubs.acs.org/doi/10.1021/acs.est.7b01613}. Specifically, +this function hard-codes in the removal of endPoints that are ATG +sources with signal loss, and NVS with signal gain. Also, this function +adds some additional categories to intended_target_family and +intended_target_family_sub as described in the paper linked above. } \examples{ endPointInfo <- endPointInfo diff --git a/man/create_toxEval.Rd b/man/create_toxEval.Rd index 68c031c7..43d92340 100644 --- a/man/create_toxEval.Rd +++ b/man/create_toxEval.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/create_toxEval.R \name{create_toxEval} \alias{create_toxEval} -\title{create_toxEval} +\title{Load and check toxEval data} \usage{ create_toxEval(excel_file_path) } @@ -10,8 +10,52 @@ create_toxEval(excel_file_path) \item{excel_file_path}{Path to Excel file that contains at least 3 tabs: Data, Chemicals, and Sites, and could optionally contain Exclude and Benchmarks} } +\value{ +list of 3 data frames, potentially up to 5. The guaranteed data +frames are chem_data (containing at least the columns: "CAS", "SiteID", "Value", "Sample Date"), +chem_info (containing at least the columns: "CAS", "Class"), +chem_site (containing at least the columns: "SiteID", "Short Name", would need "dec_lat" and "dec_lon" for shiny app). +The optional data frames are exclusions (containing at least the columns: "CAS", "endPoint"), +and benchmarks (containing at least the columns: "CAS", "endPoint","ACC_value","chnm") +} \description{ -create_toxEval +This function requires a path to a single Excel file. The Excel +file should include 3 mandatory tabs named "Data", "Chemicals", and "Sites". +Additionally there are 2 optional tabs: "Exclude" and "Benchmarks". This function +will load each sheet, creating a data frame for each sheet. It will +perform basic checks on the data to make sure there are the required columns in +each tab. +} +\details{ +The Data tab needs to have columns "CAS", "SiteID", "Value", "Sample Date". +The "Value" column is assumed to be concentration measurements in ug/L. "Sample Date" +can be either a date or date/time or an integer. Any other column can be included, +but won't be used in general toxEval functions. + +The Chemical tab needs to have columns "CAS", "Class". The "CAS" in this +tab must exactly match the "CAS" in the Data tab. The "Class" designation +allows the data to be grouped in a user-specified way. For example, you +may want to explore the difference between pesticides and herbicides. + +The Sites tab needs to have the columns "SiteID", "Short Name", and for the Shiny application +"dec_lat","dec_lon". The "SiteID" column in this tab must match exactly +the "SiteID" column in the Data tab. + +The optional tab Exclude needs to have the columns "CAS", "endPoint". These +are used to exclude particular chemicals (via CAS), ToxCast endpoints (via endPoint), +or a unique chemical/endpoint combination. + +The optional tab Benchmarks needs to have columns "CAS", "endPoint","ACC_value","chnm". This +tab is used to over-ride the functions using ToxCast endpoints, allowing the user +to import endpoint information from potentially other sources. It +could also be useful for reproducing results in the future (for example, +if ToxCast updates their data, you could use this tab to run the analysis +on the older "v2" version). + +For more information, see the "User Guide" vignette. + +All remaining toxEval functions will expect the data to be supplied +via the list that is returned from this function. } \examples{ path_to_tox <- system.file("extdata", package="toxEval") diff --git a/man/endPointInfo.Rd b/man/endPointInfo.Rd index bdf15b9b..25193e56 100644 --- a/man/endPointInfo.Rd +++ b/man/endPointInfo.Rd @@ -5,7 +5,13 @@ \alias{endPointInfo} \title{Endpoint information from ToxCast} \description{ -Downloaded on October 2015 from ToxCast dashboard +Downloaded on October 2015 from ToxCast. The file name of the +raw data was "Assay_Summary_151020.csv" from the zip file +"Assay_Information_Oct_2015.zip". At the time +of toxEval package release, this information was found: +\url{https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data} +in the section marked "Download Assay Information", in the +ToxCast & Tox21 high-throughput assay information data set. } \examples{ endPointInfo <- endPointInfo diff --git a/man/endpoint_table.Rd b/man/endpoint_table.Rd index 16311655..85da5dc2 100644 --- a/man/endpoint_table.Rd +++ b/man/endpoint_table.Rd @@ -32,12 +32,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data bio_table <- endpoint_table(chemicalSummary, category = "Biological") class_table <- endpoint_table(chemicalSummary, category = "Chemical Class") chem_table <- endpoint_table(chemicalSummary, category = "Chemical") } +} diff --git a/man/ex_chemSum.Rd b/man/ex_chemSum.Rd deleted file mode 100644 index b44a77c4..00000000 --- a/man/ex_chemSum.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toxEval.R -\docType{data} -\name{ex_chemSum} -\alias{ex_chemSum} -\title{Example chemicalSummary} -\description{ -Example data representing data from the sample Excel file found in -\code{system.file("extdata", package="toxEval")}, OWC_data_fromSup.xlsx -} -\examples{ -head(ex_chemSum) -} -\keyword{data} -\keyword{quality} -\keyword{water} diff --git a/man/exclude_points.Rd b/man/exclude_points.Rd index 546514de..e6e91e38 100644 --- a/man/exclude_points.Rd +++ b/man/exclude_points.Rd @@ -29,12 +29,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data exclusion <- data.frame(CAS = c("134-62-3","486-56-6"), endPoint = c("", "TOX21_p53_BLA_p3_viability"), stringsAsFactors = FALSE) chemicalSummary <- exclude_points(chemicalSummary, exclusion) } +} diff --git a/man/explore_endpoints.Rd b/man/explore_endpoints.Rd index 16b0f2c0..5bb90cfb 100644 --- a/man/explore_endpoints.Rd +++ b/man/explore_endpoints.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/explore_endpoints.R \name{explore_endpoints} \alias{explore_endpoints} -\title{Explore endpoint groupings} +\title{Explore data in the Shiny Application} \usage{ explore_endpoints(browse = TRUE) } @@ -10,7 +10,11 @@ explore_endpoints(browse = TRUE) \item{browse}{use browser for map rendering} } \description{ -Open an interactive app +Open an interactive app in a browser. See the vignette 'User Guide' +for more details. Using this function is a quick and convenient way +to explore your data. For more customization, the R-code to +produce each graph and table is displayed in the app. That is +a good starting-point for a custom analysis. } \examples{ \dontrun{ diff --git a/man/filter_groups.Rd b/man/filter_groups.Rd index a87401b6..ed785be6 100644 --- a/man/filter_groups.Rd +++ b/man/filter_groups.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/filter_endPoint_info.R \name{filter_groups} \alias{filter_groups} -\title{filter_groups} +\title{Filter endPoints based on groups and assays.} \usage{ filter_groups(ep, groupCol = "intended_target_family", assays = c("ATG", "NVS", "OT", "TOX21", "CEETOX", "APR", "CLD", "TANGUAY", "NHEERL_PADILLA", @@ -15,12 +15,20 @@ filter_groups(ep, groupCol = "intended_target_family", assays = c("ATG", \item{groupCol}{character name of column to use as a group catetory} \item{assays}{vector of assays to use. Possible values are "ATG","NVS","OT","TOX21","CEETOX", "APR", "BSK", -"CLD","TANGUAY","NHEERL_PADILLA","NCCT_SIMMONS","ACEA"} +"CLD","TANGUAY","NHEERL_PADILLA","NCCT_SIMMONS","ACEA". By default, the +"BSK" (BioSeek) assay is removed.} \item{remove_groups}{vector of groups to remove} } \description{ -Clean up the endPointInfo table from toxCast. Filtering and cleaning based on ES&T (cite Dan/Brett's paper) +This function takes the data frame from \code{\link{endPointInfo}} +and filters the endpoints in 3 steps. First, the user specifies +the "groupCol" which is a column header from \code{\link{endPointInfo}}. +The default category is intended_target_family. Second, the user specifies the assays to use. By default, the BioSeek +set of assays are removed. Finally, the user can also choose to remove +specific group from the category. The default is to remove "Background Measurement" +and "Undefined", but it is a good idea to check if other groups may +not be relevant to the study. } \examples{ endPointInfo <- endPointInfo diff --git a/man/getMapInfo.Rd b/man/getMapInfo.Rd index 35cee87a..7ae71c5d 100644 --- a/man/getMapInfo.Rd +++ b/man/getMapInfo.Rd @@ -34,9 +34,6 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data mapData <- getMapInfo(chemicalSummary, tox_list$chem_site, "Biological") } +} diff --git a/man/get_ACC.Rd b/man/get_ACC.Rd index bbbcee4d..ffaf4f90 100644 --- a/man/get_ACC.Rd +++ b/man/get_ACC.Rd @@ -2,15 +2,19 @@ % Please edit documentation in R/get_ACC.R \name{get_ACC} \alias{get_ACC} -\title{get_ACC} +\title{Get the ACC values for a selection of chemicals} \usage{ get_ACC(CAS) } \arguments{ -\item{CAS}{vector of CAS} +\item{CAS}{vector of CAS.} +} +\value{ +data frame with columns CAS, chnm, flags, endPoint, ACC, MlWt, and ACC_value } \description{ -Get ACC values for vector of CAS's +Data from the ToxCast are included in the toxEval package. This data is +called and filtered based on the provided vector of CAS values. } \examples{ CAS <- c("121-00-6","136-85-6","80-05-7","84-65-1","5436-43-1","126-73-8") diff --git a/man/get_chemical_summary.Rd b/man/get_chemical_summary.Rd index 3e330f7e..3203ac54 100644 --- a/man/get_chemical_summary.Rd +++ b/man/get_chemical_summary.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/get_chemical_summary.R \name{get_chemical_summary} \alias{get_chemical_summary} -\title{get_chemical_summary} +\title{Create a chemical summary of the data.} \usage{ get_chemical_summary(tox_list, ACClong = NULL, filtered_ep = "All", chem.data = NULL, chem.site = NULL, chem.info = NULL, @@ -12,25 +12,37 @@ get_chemical_summary(tox_list, ACClong = NULL, filtered_ep = "All", \item{tox_list}{list with data frames for chem_data, chem_info, chem_site, and optionally exclusions and benchmarks. Created with \code{\link{create_toxEval}}} -\item{ACClong}{data frame with at least columns: CAS, chnm, endPoint, ACC_value} +\item{ACClong}{data frame with at least columns: CAS, chnm, endPoint, ACC_value. To use data +provided by this package from ToxCast, use the \code{\link{get_ACC}} function. You may wish +to remove endPoints with specific flags using the \code{\link{remove_flags}} function.} \item{filtered_ep}{data frame with colums: endPoints, groupCol. Default is \code{"All"}, where no filtering occurs.} \item{chem.data}{OPTIONAL data frame with (at least) columns: CAS, SiteID, Value. Default is \code{NULL}. -Will over-ride what is in tox_list.} +The argument will over-ride what is in tox_list.} \item{chem.site}{OPTIONAL data frame with (at least) columns: SiteID, Short Name. Default is \code{NULL}. -Will over-ride what is in tox_list.} +The argument will over-ride what is in tox_list.} \item{chem.info}{OPTIONAL data frame with (at least) columns: CAS, class. Default is \code{NULL}. -Will over-ride what is in tox_list.} +The argument will over-ride what is in tox_list.} \item{exclusion}{OPTIONAL data frame with (at least) columns: CAS and endPoint. Default is \code{NULL}. -Will over-ride what is in tox_list.} +The argument will over-ride what is in tox_list.} } \description{ -Get ACC values for vector of CAS's +This function takes the measured user data from the output of \code{\link{create_toxEval}}, +and joins the data with the endPoint information provided by ToxCast. +Data from ToxCast is included with this package, but alternative +benchmark data can be provided to perform the same "toxEval" analysis. +} +\details{ +To use the data provided by the package, a sample workflow is shown below +in the examples. It includes getting the ToxCast (ACC) values that will +be used to calculate the EAR, filtering out the endToints that should +be ignored based on "flags" in the data, and filtering out any groups +that may not be important to the analysis at hand. } \examples{ path_to_tox <- system.file("extdata", package="toxEval") @@ -38,7 +50,7 @@ file_name <- "OWC_data_fromSup.xlsx" full_path <- file.path(path_to_tox, file_name) tox_list <- create_toxEval(full_path) - +\dontrun{ ACClong <- get_ACC(tox_list$chem_info$CAS) ACClong <- remove_flags(ACClong) @@ -46,5 +58,5 @@ cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - +} } diff --git a/man/graph_chem_data.Rd b/man/graph_chem_data.Rd index 8e483acf..5b8cbce2 100644 --- a/man/graph_chem_data.Rd +++ b/man/graph_chem_data.Rd @@ -31,9 +31,7 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data + graphData <- graph_chem_data(chemicalSummary) } +} diff --git a/man/makeMap.Rd b/man/makeMap.Rd index 55f397a5..8468f5e2 100644 --- a/man/makeMap.Rd +++ b/man/makeMap.Rd @@ -35,12 +35,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data makeMap(chemicalSummary, tox_list$chem_site, "Biological") makeMap(chemicalSummary, tox_list$chem_site, "Chemical Class") makeMap(chemicalSummary, tox_list$chem_site, "Chemical") } +} diff --git a/man/plot_chemical_boxplots.Rd b/man/plot_chemical_boxplots.Rd index 5dfc1438..9ababac9 100644 --- a/man/plot_chemical_boxplots.Rd +++ b/man/plot_chemical_boxplots.Rd @@ -36,10 +36,7 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data plot_chemical_boxplots(chemicalSummary) } +} diff --git a/man/plot_heat_chemicals.Rd b/man/plot_heat_chemicals.Rd index 27aa5f44..57883b30 100644 --- a/man/plot_heat_chemicals.Rd +++ b/man/plot_heat_chemicals.Rd @@ -29,11 +29,6 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. - -chemicalSummary <- ex_chemSum #loading example data graphData <- graph_chem_data(chemicalSummary) plot_heat_chemicals(graphData, tox_list$chem_site) @@ -67,3 +62,4 @@ tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`, plot_heat_chemicals(graphData, tox_list$chem_site) } +} diff --git a/man/plot_tox_boxplots.Rd b/man/plot_tox_boxplots.Rd index 518590c4..4843c7ad 100644 --- a/man/plot_tox_boxplots.Rd +++ b/man/plot_tox_boxplots.Rd @@ -39,12 +39,8 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. - -chemicalSummary <- ex_chemSum #loading example data plot_tox_boxplots(chemicalSummary, "Biological") plot_tox_boxplots(chemicalSummary, "Chemical Class") plot_tox_boxplots(chemicalSummary, "Chemical") } +} diff --git a/man/plot_tox_endpoints.Rd b/man/plot_tox_endpoints.Rd index 5e4beae7..c05c027b 100644 --- a/man/plot_tox_endpoints.Rd +++ b/man/plot_tox_endpoints.Rd @@ -39,11 +39,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) + +plot_tox_endpoints(chemicalSummary, filterBy = "Cell Cycle") +plot_tox_endpoints(chemicalSummary, category = "Chemical Class", filterBy = "PAHs") +plot_tox_endpoints(chemicalSummary, category = "Chemical", filterBy = "Atrazine") } -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data - plot_tox_endpoints(chemicalSummary, filterBy = "Cell Cycle") - plot_tox_endpoints(chemicalSummary, category = "Chemical Class", filterBy = "PAHs") - plot_tox_endpoints(chemicalSummary, category = "Chemical", filterBy = "Atrazine") } diff --git a/man/plot_tox_heatmap.Rd b/man/plot_tox_heatmap.Rd index 546c905a..68fbcff5 100644 --- a/man/plot_tox_heatmap.Rd +++ b/man/plot_tox_heatmap.Rd @@ -27,7 +27,7 @@ Plot heat map path_to_tox <- system.file("extdata", package="toxEval") file_name <- "OWC_data_fromSup.xlsx" full_path <- file.path(path_to_tox, file_name) - +\dontrun{ tox_list <- create_toxEval(full_path) ACClong <- get_ACC(tox_list$chem_info$CAS) @@ -70,3 +70,4 @@ plot_tox_heatmap(chemicalSummary, plot_tox_heatmap(chemicalSummary, tox_list$chem_site, category = "Chemical Class") plot_tox_heatmap(chemicalSummary, tox_list$chem_site, category = "Chemical") } +} diff --git a/man/plot_tox_stacks.Rd b/man/plot_tox_stacks.Rd index 1a0ef1a6..adf8de35 100644 --- a/man/plot_tox_stacks.Rd +++ b/man/plot_tox_stacks.Rd @@ -38,12 +38,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data - + plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Biological") plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Chemical Class") plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Chemical", include_legend = FALSE) } +} diff --git a/man/remove_flags.Rd b/man/remove_flags.Rd index c9006113..1f9e6298 100644 --- a/man/remove_flags.Rd +++ b/man/remove_flags.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/get_chemical_summary.R \name{remove_flags} \alias{remove_flags} -\title{remove_flags} +\title{Remove endpoints with specific flags from data} \usage{ remove_flags(ACClong, flagsShort = c("Borderline", "OnlyHighest", "GainAC50", "Biochemical")) @@ -14,7 +14,18 @@ remove_flags(ACClong, flagsShort = c("Borderline", "OnlyHighest", "GainAC50", "Borderline", "OnlyHighest", "OneAbove","Noisy", "HitCall", "GainAC50", "Biochemical"} } \description{ -Remove endpoints with specific flags from data +Remove endpoints with specific flags associated with the ACC values. The set +of flags that are included are: +\tabular{ll}{ +Flag \tab flagsShort\cr +Borderline active \tab Borderline \cr +Only highest conc above baseline, active \tab OnlyHighest \cr +Only one conc above baseline, active \tab OneAbove \cr +Noisy data \tab Noisy \cr +Hit-call potentially confounded by overfitting \tab HitCall \cr +Gain AC50 < lowest conc & loss AC50 < mean conc \tab GainAC50 \cr +Biochemical assay with < 50% efficacy \tab Biochemical \cr +} } \examples{ CAS <- c("121-00-6","136-85-6","80-05-7","84-65-1","5436-43-1","126-73-8") diff --git a/man/table_endpoint_hits.Rd b/man/table_endpoint_hits.Rd index e79f8292..d061dfca 100644 --- a/man/table_endpoint_hits.Rd +++ b/man/table_endpoint_hits.Rd @@ -34,12 +34,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data - + table_endpoint_hits(chemicalSummary, category = "Biological") table_endpoint_hits(chemicalSummary, category = "Chemical Class") table_endpoint_hits(chemicalSummary, category = "Chemical") } +} diff --git a/man/table_tox_endpoint.Rd b/man/table_tox_endpoint.Rd index 26859f52..9042427d 100644 --- a/man/table_tox_endpoint.Rd +++ b/man/table_tox_endpoint.Rd @@ -34,12 +34,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data - + table_tox_endpoint(chemicalSummary, category = "Biological") table_tox_endpoint(chemicalSummary, category = "Chemical Class") table_tox_endpoint(chemicalSummary, category = "Chemical") } +} diff --git a/man/table_tox_rank.Rd b/man/table_tox_rank.Rd index dfdf78bd..ff3f51b7 100644 --- a/man/table_tox_rank.Rd +++ b/man/table_tox_rank.Rd @@ -34,11 +34,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data + table_tox_rank(chemicalSummary, category = "Biological") table_tox_rank(chemicalSummary, category = "Chemical Class") table_tox_rank(chemicalSummary, category = "Chemical") } +} diff --git a/man/table_tox_sum.Rd b/man/table_tox_sum.Rd index 099b8270..13994733 100644 --- a/man/table_tox_sum.Rd +++ b/man/table_tox_sum.Rd @@ -34,11 +34,9 @@ ACClong <- remove_flags(ACClong) cleaned_ep <- clean_endPoint_info(endPointInfo) filtered_ep <- filter_groups(cleaned_ep) chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) -} -# The example workflow takes a bit of time to load and compute, -# so an example chemicalSummary is included pre-calculated in the package. -chemicalSummary <- ex_chemSum #loading example data + table_tox_sum(chemicalSummary, category = "Biological") table_tox_sum(chemicalSummary, category = "Chemical Class") table_tox_sum(chemicalSummary, category = "Chemical") } +} diff --git a/man/tox_chemicals.Rd b/man/tox_chemicals.Rd index d4e049c0..1db2602b 100644 --- a/man/tox_chemicals.Rd +++ b/man/tox_chemicals.Rd @@ -3,9 +3,16 @@ \docType{data} \name{tox_chemicals} \alias{tox_chemicals} -\title{tox_chemicals} +\title{ToxCast Chemical Information} \description{ -Downloaded on October 2015 from ToxCast dashboard +Downloaded on October 2015 from ToxCast. The file name of the +raw data was "TOX21IDs_v4b_23Oct2014_QCdetails.xlsx", +from the US EPA DSSTox DATA RELEASE OCTOBER 2015. At the time +of toxEval package release, this information was found: +\url{https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data} +in the section marked "Download ToxCast Chemical Information". This +was in the "ToxCast & Tox21 Chemicals Distributed Structure-Searchable Toxicity Database (DSSTox files)" +data set. } \examples{ head(tox_chemicals) diff --git a/vignettes/AOP_exploration.Rmd b/vignettes/AOP_exploration.Rmd deleted file mode 100644 index 494d2259..00000000 --- a/vignettes/AOP_exploration.Rmd +++ /dev/null @@ -1,119 +0,0 @@ ---- -title: "SI: AOP" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{Figure_3} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -## Biological influence - - - - -```{r getChems} -library(toxEval) -library(dplyr) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - - -``` - -```{r getAOPs} -AOP_crosswalk <- read.csv(file.path(path_to_tox, "AOP_crosswalk.csv"), stringsAsFactors = FALSE) -``` - -```{r sumAOP} -AOP_summaries <- chemicalSummary %>% - left_join(select(AOP_crosswalk, - endPoint=Component.Endpoint.Name, - AOP_id = AOP.., - AOP_title = AOP.Title), by="endPoint") %>% - filter(!is.na(AOP_id)) %>% - group_by(shortName,date,AOP_id, AOP_title) %>% - summarise(sumEAR = sum(EAR, na.rm = TRUE)) %>% - group_by(shortName, AOP_id, AOP_title) %>% - summarise(maxEAR = max(sumEAR, na.rm = TRUE)) %>% - group_by(AOP_id, AOP_title) %>% - summarise(sum_of_maxes = sum(maxEAR)) %>% - arrange(desc(sum_of_maxes)) - -threshold <- 1 - - -kable(filter(AOP_summaries, sum_of_maxes> threshold)) - -``` - - - -```{r sumKE} - -KE_summaries <- chemicalSummary %>% - left_join(select(AOP_crosswalk, - endPoint=Component.Endpoint.Name, - KE_id = KE., - KE_title = Key.Event.Name), by="endPoint") %>% - filter(!is.na(KE_id)) %>% - group_by(shortName,date,KE_id, KE_title) %>% - summarise(sumEAR = sum(EAR, na.rm = TRUE)) %>% - group_by(shortName, KE_id, KE_title) %>% - summarise(maxEAR = max(sumEAR, na.rm = TRUE)) %>% - group_by(KE_id, KE_title) %>% - summarise(sum_of_maxes = sum(maxEAR)) %>% - arrange(desc(sum_of_maxes)) - -threshold <- 1 - -kable(filter(KE_summaries, sum_of_maxes> threshold)) - -``` - -```{r otherStuff} - -non_AOP_summaries <- chemicalSummary %>% - anti_join(select(AOP_crosswalk, - endPoint=Component.Endpoint.Name), by="endPoint") %>% - group_by(shortName,date, endPoint) %>% - summarise(sumEAR = sum(EAR, na.rm = TRUE)) %>% - group_by(shortName, endPoint) %>% - summarise(maxEAR = max(sumEAR, na.rm = TRUE)) %>% - group_by(endPoint) %>% - summarise(sum_of_maxes = sum(maxEAR)) %>% - arrange(desc(sum_of_maxes)) - -threshold <- 1 - -kable(filter(non_AOP_summaries, sum_of_maxes> threshold)) - - -``` \ No newline at end of file diff --git a/vignettes/Endpoint_digdown.Rmd b/vignettes/Endpoint_digdown.Rmd deleted file mode 100644 index 1e49c5fe..00000000 --- a/vignettes/Endpoint_digdown.Rmd +++ /dev/null @@ -1,88 +0,0 @@ ---- -title: "Endpoint Digdown" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI7} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Digging down: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) -library(stringi) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -plot_tox_boxplots(chemicalSummary, category = "Biological") - -``` - -Now let's just look at Nuclear Receptors: - -```{r fig.height=14} -CAS_NR <- unique(chemicalSummary$CAS[chemicalSummary$Bio_category == "Nuclear Receptor"]) - -chem_data_NR <- filter(tox_list$chem_data, CAS %in% CAS_NR) -chem_info_NR <- filter(tox_list$chem_info, CAS %in% CAS_NR) - -filtered_ep <- filter_groups(cleaned_ep, groupCol = "intended_target_gene_symbol") - -chemicalSummary <- get_chemical_summary(tox_list,ACClong, filtered_ep, - chem.data = chem_data_NR, - chem.info = chem_info_NR) - -plot_tox_boxplots(chemicalSummary, category = "Biological") - -``` - -And now...we dig down to just "ESR1": - -```{r} - -CAS_PTEN <- unique(chemicalSummary$CAS[chemicalSummary$Bio_category == "ESR1"]) - -chem_data_PTEN <- filter(chem_data_NR, CAS %in% CAS_PTEN) -chem_info_PTEN <- filter(chem_info_NR, CAS %in% CAS_PTEN) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep, - chem.data = chem_data_PTEN, - chem.info = chem_info_PTEN) -plot_tox_endpoints(chemicalSummary) - -``` - - - diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd new file mode 100644 index 00000000..80c0adaf --- /dev/null +++ b/vignettes/Introduction.Rmd @@ -0,0 +1,52 @@ +--- +title: "Introduction to toxEval" +date: "`r format(Sys.time(), '%d %B, %Y')`" +output: + rmarkdown::html_vignette: + toc: true + number_sections: false +vignette: > + %\VignetteEngine{knitr::rmarkdown} + %\VignetteIndexEntry{Introduction to toxEval} + \usepackage[utf8]{inputenc} +--- + + +The `toxEval` R-package includes a set of functions to analyze, visualize, and organize measured concentration data as it relates to ToxCast data. These functions allow great flexibly for exploring the potential biological affects of measured chemicals. Also included in the package is a browser-based application made from the `shiny` R-package. There is less customization from the application, but it is a convenient way to explore the data. + +This vignette describes the process to prepare the data and use the shiny application. There is also a section on installing and updating the package, since it is not on CRAN. + +# Introduction + +The U.S. EPA's Toxicity Forecaster ToxCast is a database with thousands of chemicals that can give biological relevance to measured concentrations. The `toxEval` package attempts to simplify the workflow for exploring data as it relates to the benchmark data. By default, the workflow uses the ToxCast information, but there is also a way to provide alternative benchmarks. This could be useful for comparing ToxCast results with other toxicity benchmark databases. + +When using the ToxCast endPoints for analysis, it will be important to have at least a minimal understanding of what ToxCast data is, and which ToxCast data is relevant to your study. There are many useful resources here. There is also a tool called the TosCast Dashboard that has a wealth of information on ToxCast data. + +So what are we doing? First, we calculate an Exposure-Activity Ratio (EAR) for each measurement. Then we can explore the EARs based on a wide variety of groupings to explore the data. + +## Exposure-Activity Ratio + +A Exposure-Activity Ratio (EAR) is the measured concentration divided by a concentration that was determined to cause some activity. The ToxCast database (as provided in the current version of `toxEval`) provides 991 "endPoints" for more than 6575 chemicals. Each endPoint is a single test that was done to detect some form of activity. + +In order to get consistent EAR results, it is important to use the correct units. The `toxEval` package assumes all measured concentration is repored in micrograms per liter (ug/L). ToxCast data is reported in log(uM), so the `toxEval` package automatically performs the unit conversion. + +ToxCast uses high-throughput assays to create does-response curves for each of these chemical/endPoint combinations. Activity concentration at cutoff (ACC) values of these does response curves are provided in the `toxEval` package. ACC was choosen over the chemical-specific half-maximal activity concentration (AC50) as described in Blackwell 2017. + +## What is an "endPoint"? + +An endPoint is "associated with the perturbation of specific biological processes identified for the confirmation or monitoring of predicted site-specific hazards" Blackwell 2017. That means a very specific action was tested, and concentration at which that activity was observed was determined. For ToxCast endPoints, that is via the ACC value in a dose response curve. + +## Grouping options + +Often, a single endPoint is too specific to be of much interest. ToxCast has provided tables that group individual endPoints into more generalized categories. The grouping summary table is included in `toxEval` and can be explored via the `endPointInfo` data: + +```{r endPointInfo, eval=FALSE} +library(toxEval) +endPointInfo <- endPointInfo +``` + +See the help file `?endPointInfo` for specifics on how the table was downloaded. + +Throughout the `toxEval` analysis, there are graphing and table functions that will summarize EARs based on either "Biological" groupings (as defined by a group of endPoints) or "Chemical Class" groupings (as defined by a group of chemicals). + +The default grouping of ToxCast endPoints is "intended_target_family", but depending on the analysis, it may be more appropriate to use other grouping categories. diff --git a/vignettes/PrepareData.Rmd b/vignettes/PrepareData.Rmd new file mode 100644 index 00000000..ec691ac5 --- /dev/null +++ b/vignettes/PrepareData.Rmd @@ -0,0 +1,95 @@ +--- +title: "Preparing toxEval Data" +date: "`r format(Sys.time(), '%d %B, %Y')`" +output: + rmarkdown::html_vignette: + toc: true + number_sections: false + fig_caption: yes + fig_height: 10 + fig_width: 7 +vignette: > + %\VignetteEngine{knitr::rmarkdown} + %\VignetteIndexEntry{Prepare Data} + \usepackage[utf8]{inputenc} +--- + +# Preparing the data + +The data should be prepared in a Microsoft ™ Excel file. There are 3 mandatory sheets (also known as tabs), and 2 optional sheets. The sheets should appear like this (although the order is not important): + + + +Each sheet has mandatory columns; the order of the columns is not important, but the names of the columns is important. Additional columns can be included but will be ignored. The top row of each sheet should contain the column names (headers), no rows should be initially skipped, and no titles or comment rows should precede the data. + +## Data + +The "Data" tab needs to have at a minimum 4 columns: "CAS", "SiteID", "Value", and "Sample Date". They can be in any order, but the first row of sheet should be the header (column names). + +* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose "Format Cells", and choose "Text" as the category to assure they retain their format. The unique CAS values in this column *must* match with the CAS values in the "Chemicals" sheet. + +* SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique "SiteID" values *must* match the "SiteID" values in the "Sites" sheet. There is no requirements on formatting for this column. + +* Value: This column is numeric. It should be the concentration of the chemical in $\mu$g/L. + +* Sample Date: Currently this column only serves to identify unique samples. Dates and times as formatted in Excel, as well as integer values are acceptable. The actual date and times are not currently used to perform any time series analysis. + +Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis. + +As an example, the first several rows of a minimal example would look like this: + + + +## Chemicals + +The "Chemicals" tab needs to have at a minimum 2 columns: "CAS" and "Class". They can be in any order, but the first row of sheet should be the header (column names). + +* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose "Format Cells", and choose "Text" as the category to assure they retain their format. The unique CAS values in this column *must* match with the CAS values in the "Data" sheet. + +* Class: A character column defining the class of chemicals. Most `toxEval` functions will allow groupings by either chemical, class (as defined here), or biological grouping. + +Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis. + +## Sites + +The "Sites" tab needs to have at a minimum 4 columns: "SiteID", "Short Name", "dec_lon", and "dec_lat". They can be in any order, but the first row of sheet should be the header (column names). + +* SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique "SiteID" values *must* match the "SiteID" values in the "Data" sheet. There is no requirements on formatting for this column. + +* Short Name: This character column is the name associated with the SiteID that will be used in the graphs and tables. The names should be sufficiently descriptive, but as short as possible to optimize the area on the graph devoted to the data and not the labels. + +* dec_lon: This numeric column is the decimal longitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most `toxEval` functions will not require this column. + +* dec_lat: This numeric column is the decimal latitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most `toxEval` functions will not require this column. + +Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis. + +## Exclude + +The "Exclude" tab is an optional tab. If used, the "Exclude" tab needs to have at a minimum 2 columns: "CAS" and "endPoint". They can be in any order, but the first row of sheet should be the header (column names). + +* endPoint: A character column define a specific ToxCast endpoint to ignore. IF the "CAS" in the corresponding row is empty, the endPoint will be completely excluded from the `toxEval` analysis. For example, maybe it was decided that TOX21_p53_BLA_p3_ratio was not an appropriate endPoint to consider in the analysis. We can exclude all TOX21_p53_BLA_p3_ratio endPoints by adding it to the Exclude tab. IF the "CAS" in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis. + +* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. IF the "endPoint" in the corresponding row is empty, the chemical will be completely excluded from the `toxEval` analysis. For example, maybe it was discovered that DEET was contaminated in the sampling method. We can exclude all DEET measurements by adding 134-62-3 to the Exclude tab. IF the "endPoint" in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis. + +There could be a variety of reasons to exclude chemical/endpoint combinations. This will be discussed further in section TODO: XXXXXXXXX. + +Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis. + +## Benchmarks + +The "Benchmarks" tab is an optional tab. The benchmark tab can be used to over-ride the ToxCast endpoints. For example, there may be a need to perform similar `toxEval` analysis using EPA benchmarks instead of ToxCast endpoints. + +For the functions to work seamlessly with all `toxEval` functions, the "Benchmarks" tab needs to have at a minimum 5 columns: "CAS", "Chemical", "endPoint", "Value", and "groupCol". They can be in any order, but the first row of sheet should be the header (column names). + +* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. + +* Chemical: A character column defining the name of the chemicals. + +* endPoint: A character column naming the benchmark + +* Value: The concentration (in identical units as what is reported in the "Data" tab) of the benchmark. + +* groupCol: A character column that groups endpoints. This is analogous to the Biological groupings from the ToxCast analysis. + +Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis. diff --git a/vignettes/SI2_DetectionLevels.Rmd b/vignettes/SI2_DetectionLevels.Rmd deleted file mode 100644 index 39d2d36c..00000000 --- a/vignettes/SI2_DetectionLevels.Rmd +++ /dev/null @@ -1,78 +0,0 @@ ---- -title: "SI 2: Detection Levels" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI2} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Using the detection levels as concentrations: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) -library(grid) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -# Substitute max LDL or MDL for actual values: - -tox_list$chem_data <- tox_list$chem_data %>% - left_join(select(tox_list$chem_info, - CAS, - MDL = `Maximum method detection level`, - LDL = `Maximum laboratory reporting level`), - by="CAS") %>% - rowwise() %>% - mutate(Value = max(MDL, LDL, na.rm = TRUE)) %>% - select(SiteID, `Sample Date`, CAS, Value) %>% - distinct() - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -#Trim some names: -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance" - -# Basically...need to swap endpoint and site to take advantage of plot_tox_boxplots code - -chemicalSummary$site <- chemicalSummary$endPoint - -plot_DL <- plot_tox_boxplots(chemicalSummary, "Chemical") - -plot_DL -grid.text("# Endpoints:", - x = unit(.375, "npc"), - y = unit(.992, "npc"), gp=gpar(fontsize=7)) - -``` \ No newline at end of file diff --git a/vignettes/SI3_heatMap.Rmd b/vignettes/SI3_heatMap.Rmd deleted file mode 100644 index a642df3b..00000000 --- a/vignettes/SI3_heatMap.Rmd +++ /dev/null @@ -1,83 +0,0 @@ ---- -title: "SI 3:Heat Map" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI3} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 10) -``` - -Using the detection levels as concentrations: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -#Trim some names: -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance" - -# Order the Great Lakes: -tox_list$chem_site$site_grouping <- factor(tox_list$chem_site$site_grouping, - levels=c("Lake Superior", - "Lake Michigan", - "Lake Huron", - "Lake Erie", - "Lake Ontario")) - -# Order sites: - sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", - "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", - "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", - "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", - "Manistee","Fox","Oconto","Peshtigo","Menominee", - "Indian","Cheboygan","Ford","Escanaba","Manistique", - "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", - "Clinton","Rouge","HuronMI","Raisin","Maumee", - "Portage","Sandusky","HuronOH","Vermilion","BlackOH", - "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", - "Genesee","Oswego","BlackNY","Oswegatchie","Grass", - "Raquette","StRegis") - - tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`, - levels = sitesOrdered) - - - plot_tox_heatmap(chemicalSummary, - tox_list$chem_site, - category = "Chemical") - -``` \ No newline at end of file diff --git a/vignettes/SI4_SiteCounts.Rmd b/vignettes/SI4_SiteCounts.Rmd deleted file mode 100644 index 6636b75e..00000000 --- a/vignettes/SI4_SiteCounts.Rmd +++ /dev/null @@ -1,75 +0,0 @@ ---- -title: "SI 4: Site Counts" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI4} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Using the detection levels as concentrations: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -#Trim some names: -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance" - -tableData <- chemicalSummary %>% - group_by(site, date, chnm) %>% - summarize(sumEAR = sum(EAR)) %>% - group_by(site, chnm) %>% - summarize(meanEAR = max(sumEAR)) %>% - group_by(chnm) %>% - summarize(nSites = sum(meanEAR > 10^-3)) %>% - data.frame() %>% - arrange(desc(nSites)) %>% - filter(nSites > 1) - -tableData$chnm <- factor(tableData$chnm, levels = tableData$chnm) - -chemPlot <- ggplot(tableData)+ - geom_bar(aes(x=chnm, y=nSites),stat = "identity",fill = "steelblue") + - theme_bw() + - xlab("") + - ylab("Number of Sites\n with EARmax > 0.001") + - theme(axis.text.x = element_text( angle = 90,vjust=0.5,hjust = 1)) - -chemPlot - -``` \ No newline at end of file diff --git a/vignettes/SI5_heatMapBiologics.Rmd b/vignettes/SI5_heatMapBiologics.Rmd deleted file mode 100644 index 6f86c27d..00000000 --- a/vignettes/SI5_heatMapBiologics.Rmd +++ /dev/null @@ -1,79 +0,0 @@ ---- -title: "SI 5: Biological Heat Map" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI5} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Using the detection levels as concentrations: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -# Order the Great Lakes: -tox_list$chem_site$site_grouping <- factor(tox_list$chem_site$site_grouping, - levels=c("Lake Superior", - "Lake Michigan", - "Lake Huron", - "Lake Erie", - "Lake Ontario")) - -# Order sites: - sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", - "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", - "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", - "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", - "Manistee","Fox","Oconto","Peshtigo","Menominee", - "Indian","Cheboygan","Ford","Escanaba","Manistique", - "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", - "Clinton","Rouge","HuronMI","Raisin","Maumee", - "Portage","Sandusky","HuronOH","Vermilion","BlackOH", - "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", - "Genesee","Oswego","BlackNY","Oswegatchie","Grass", - "Raquette","StRegis") - - tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`, - levels = sitesOrdered) - - plot_tox_heatmap(chemicalSummary, - tox_list$chem_site, - category = "Biological", - manual_remove = "Undefined") - - -``` \ No newline at end of file diff --git a/vignettes/SI6_SubFamilies.Rmd b/vignettes/SI6_SubFamilies.Rmd deleted file mode 100644 index 48b07fbe..00000000 --- a/vignettes/SI6_SubFamilies.Rmd +++ /dev/null @@ -1,134 +0,0 @@ ---- -title: "SI 6: Sub-families" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI6} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Using the detection levels as concentrations: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) -library(stringi) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -chemicalSummary <- chemicalSummary %>% - left_join(select(endPointInfo, - endPoint=assay_component_endpoint_name, - family = intended_target_family, - subFamily = intended_target_family_sub), by="endPoint") - -graphData <- chemicalSummary %>% - filter(!is.na(subFamily)) %>% - group_by(site, Bio_category, subFamily, date) %>% - summarize(sumEAR = sum(EAR)) %>% - group_by(site, Bio_category, subFamily) %>% - summarize(meanEAR = max(sumEAR)) %>% - data.frame() %>% - mutate(subFamily = stri_trans_totitle(subFamily)) - -orderSub <- graphData %>% - group_by(Bio_category) %>% - summarise(median = median(meanEAR[meanEAR != 0])) %>% - data.frame() %>% - arrange(desc(median)) - -orderGroups <- graphData %>% - group_by(subFamily, Bio_category) %>% - summarise(median = quantile(meanEAR[meanEAR != 0],0.5)) %>% - data.frame() %>% - mutate(Bio_category = factor(Bio_category, levels=orderSub$Bio_category)) %>% - arrange(Bio_category, desc(median)) - -orderedSub <- rev(orderGroups$subFamily)[rev(orderGroups$subFamily) %in% unique(graphData$subFamily)] -orderedSub <- unique(orderedSub) - -graphData$subFamily <- factor(graphData$subFamily, - levels = orderedSub) - -graphData$Bio_category <- factor(graphData$Bio_category, - levels = orderSub$Bio_category) - -cbValues <- c("#DCDA4B","#999999","#00FFFF","#CEA226","#CC79A7","#4E26CE", - "#FFFF00","#78C15A","#79AEAE","#FF0000","#00FF00","#B1611D", - "#FFA500","#F4426e", "#4286f4","red","pink") - -countNonZero <- graphData %>% - group_by(subFamily) %>% - summarise(nonZero = as.character(length(unique(site[meanEAR>0])))) %>% - data.frame() - -subPlot <- ggplot(graphData)+ - scale_y_log10("Maximum EAR Per Site",labels=fancyNumbers)+ - geom_boxplot(aes(x=subFamily, y=meanEAR,fill = Bio_category), - lwd=0.1,outlier.size=1) + - coord_flip() + - theme_bw() + - xlab("") + - theme(plot.background = element_rect(fill = "transparent",colour = NA), - axis.text = element_text(size=8, color = "black"), - axis.text.y = element_text(vjust = 0.2), - axis.text.x = element_text(vjust = 0, margin = margin(-0.5,0,0,0)), - axis.title = element_text(size=10)) + - scale_fill_manual(values = cbValues, drop=TRUE) + - guides(fill=guide_legend(ncol=6)) + - theme(legend.position="bottom", - legend.justification = "left", - legend.background = element_rect(fill = "transparent", colour = "transparent"), - legend.title=element_blank(), - legend.text = element_text(size=8), - legend.key.height = unit(1,"line")) - -plot_info <- ggplot_build(subPlot) -layout_stuff <- plot_info$layout - -if(packageVersion("ggplot2") >= "2.2.1.9000"){ - xmin <- 10^(layout_stuff$panel_scales_y[[1]]$range$range[1]) -} else { - xmin <- 10^(layout_stuff$panel_ranges[[1]]$x.range[1]) -} - - -subPlot <- subPlot + - geom_text(data=countNonZero, aes(x=subFamily, y=xmin,label=nonZero),size=3) - -subPlot - - -``` - -The "Xenobiotic Metabolism" has multiple families because we switched some by hand in the clean_endPoint_info function. diff --git a/vignettes/SI7_ClassStacks.Rmd b/vignettes/SI7_ClassStacks.Rmd deleted file mode 100644 index 0594b37e..00000000 --- a/vignettes/SI7_ClassStacks.Rmd +++ /dev/null @@ -1,87 +0,0 @@ ---- -title: "SI7: Class" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI7} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 10) -``` - - - - - -```{r } -library(toxEval) -library(dplyr) -library(ggplot2) -library(grid) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", - "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", - "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", - "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", - "Manistee","Fox","Oconto","Peshtigo","Menominee", - "Indian","Cheboygan","Ford","Escanaba","Manistique", - "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", - "Clinton","Rouge","HuronMI","Raisin","Maumee", - "Portage","Sandusky","HuronOH","Vermilion","BlackOH", - "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", - "Genesee","Oswego","BlackNY","Oswegatchie","Grass", - "Raquette","StRegis") - -chem_site <- tox_list$chem_site %>% - mutate(`Short Name` = factor(`Short Name`, levels = sitesOrdered)) %>% - mutate(site_grouping = factor(site_grouping, levels = c("Lake Superior", - "Lake Michigan", - "Lake Huron", - "Lake Erie", - "Lake Ontario"))) - -for(class in unique(chemicalSummary$Class)){ - sub_class <- filter(chemicalSummary, Class %in% class) - - upperPlot <- plot_tox_stacks(sub_class, - chem_site, - category = "Chemical") - upperPlot <- upperPlot + - ggtitle(class) - - print(upperPlot) - grid.text("# Samples:", - x = unit(.03, "npc"), - y = unit(.205, "npc"), gp=gpar(fontsize=7)) -} - - - -``` \ No newline at end of file diff --git a/vignettes/SI_Site_Tables.Rmd b/vignettes/SI_Site_Tables.Rmd deleted file mode 100644 index 10095f70..00000000 --- a/vignettes/SI_Site_Tables.Rmd +++ /dev/null @@ -1,156 +0,0 @@ ---- -title: "SI Table 5: SI-5 Site Counts" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI Table 5} - \usepackage[utf8]{inputenc} ---- - - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Get data ready: - - -```{r } -library(toxEval) -library(dplyr) -library(tidyr) -library(DT) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -#Trim some names: -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance" - - -file_name <- "AOP_crosswalk.csv" -full_path <- file.path(path_to_tox, file_name) - -AOP_crosswalk <- read.csv(full_path, stringsAsFactors = FALSE) - -chemicalSummary <- chemicalSummary %>% - left_join(select(endPointInfo, - endPoint=assay_component_endpoint_name, - subFamily=intended_target_family_sub, - gene_symbol=intended_target_gene_symbol), by="endPoint") %>% - left_join(select(tox_list$chem_info, CAS, `Chemical Name`), by="CAS") - - -``` - -## Site tables: - -```{r } - -tableData <- chemicalSummary %>% - rename(Chemical=`Chemical Name`, - Family=Bio_category) %>% - group_by(site, endPoint, Family, subFamily, gene_symbol, Chemical) - -max_Samples <- tableData %>% - summarize(sumEAR = sum(EAR)) %>% #Sum per date - slice(which.max(sumEAR)) %>% # Gets max per date - filter(sumEAR > 0) %>% - data.frame() %>% - spread(Chemical, sumEAR) %>% - arrange(site, Family, subFamily, gene_symbol) %>% - select(site, Family, subFamily, gene_symbol,endPoint, everything()) %>% - mutate(maxSample = rowSums(.[-1:-5], na.rm = TRUE)) %>% - select(site, Family, subFamily, gene_symbol, endPoint, maxSample) - -tableData <- tableData %>% - summarize(maxEAR = max(EAR)) %>% - filter(maxEAR > 0) %>% - data.frame() %>% - spread(Chemical, maxEAR) %>% - arrange(site, Family, subFamily, gene_symbol) %>% - select(site, Family, subFamily, gene_symbol,endPoint, everything()) %>% - left_join(select(AOP_crosswalk, - endPoint=Component.Endpoint.Name, - AOP_id = AOP.., - AOP_title = AOP.Title), by="endPoint") %>% - left_join(max_Samples, by=c("site", "Family", "subFamily", "gene_symbol","endPoint")) %>% - select(site, Family, subFamily, gene_symbol, endPoint, AOP_id, AOP_title, maxSample, everything()) - -list_tables <- list() -chem_site <- tox_list$chem_site - -for(i in 1:nrow(chem_site)){ - - site <- chem_site$SiteID[i] - site_name <- chem_site$`Short Name`[i] - tableData_site <- tableData[tableData$site == site,] - tableData_site <- Filter(function(x)!all(is.na(x)), tableData_site) - - list_tables[[2*i-1]] <- htmltools::tags$h3(site_name) - - if(nrow(tableData_site) > 0){ - tableData2 <- select(tableData_site, -endPoint, -Family, -subFamily, -gene_symbol, -AOP_id, -AOP_title, -site, -maxSample) - tableData_site$nChems <- apply(tableData2, MARGIN = 1, function(x) sum(x>0, na.rm = TRUE)) - orderedCols <- tox_list$chem_info$`Chemical Name`[tox_list$chem_info$`Chemical Name` %in% names(tableData_site)] - - tableData_site <- tableData_site[,c("Family", "subFamily", "gene_symbol", "endPoint","AOP_id","AOP_title", "maxSample", "nChems", orderedCols)] - - dt_table <- datatable(tableData_site, rownames = FALSE,extensions = 'Buttons', - options = list(dom = 'Bfrtip', - - buttons = list('colvis', list( - extend = 'collection', - buttons = list(list(extend='csv', - filename = 'siteTable'), - list(extend='excel', - filename = 'siteTable'), - list(extend='pdf', - filename= 'siteTable')), - text = 'Download') - ))) %>% - formatSignif(columns=c("maxSample",orderedCols), digits=3) - - list_tables[[2*i]] <- dt_table - } else { - list_tables[[2*i]] <- htmltools::tags$h3("EAR never > 0") - } -} - - -``` - - -```{r } - -htmltools::tagList( - list_tables -) - -``` diff --git a/vignettes/SI_Tables.Rmd b/vignettes/SI_Tables.Rmd deleted file mode 100644 index b2eb3403..00000000 --- a/vignettes/SI_Tables.Rmd +++ /dev/null @@ -1,230 +0,0 @@ ---- -title: "SI Table 5: SI-5 Site Counts" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{SI Table 5} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -Get data ready: - - -```{r } -library(readxl) -library(toxEval) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -chem_data <- read_excel(full_path, sheet = "Data") -chem_info <- read_excel(full_path, sheet = "Chemicals") -chem_site <- read_excel(full_path, sheet = "Sites") -exclusion <- read_excel(full_path, sheet = "Exclude") - -#Trim names and order for graph: -chem_info$Class[chem_info$Class == "Antimicrobial Disinfectants"] <- "Antimicrobial" -chem_info$Class[chem_info$Class == "Detergent Metabolites"] <- "Detergent" -chem_info$Class[chem_info$Class == "Flavors and Fragrances"] <- "Flavor/Fragrance" - -chem_info$Class <- factor(chem_info$Class) - -ACClong <- get_ACC(chem_info$CAS) -ACClong <- remove_flags(ACClong) -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -``` - -## SI Table 2: Chemical Classes - -```{r warning=FALSE, message=FALSE} - -library(dplyr) -library(tidyr) -library(DT) - -chem_info_SI <- chem_info %>% - select(`OWC Class`=Class, - `Compound Name` = `Chemical Name`, - `CAS Registry Number` = CAS, - EEF_max_in.vitro_or_in.vivo, - AqT_EPA_acute, - AqT_EPA_chronic, - AqT_other_acute) %>% - mutate(Units = "ug/l") - -# write.csv(chem_info_SI, file="chem_info_SI.csv", row.names = FALSE, na = "") - -chem_info_SI <- chem_info_SI %>% - mutate(EEF_max_in.vitro_or_in.vivo = as.numeric(EEF_max_in.vitro_or_in.vivo), - AqT_EPA_acute = as.numeric(AqT_EPA_acute), - AqT_EPA_chronic = as.numeric(AqT_EPA_chronic), - AqT_other_acute = as.numeric(AqT_other_acute)) - -datatable(chem_info_SI, - rownames = FALSE, - extensions = 'Buttons', - options = list( - dom = 'Bfrtip', - buttons = list('colvis', list( - extend = 'collection', - buttons = list(list(extend='csv', - filename = 'siteTable'), - list(extend='excel', - filename = 'siteTable'), - list(extend='pdf', - filename= 'fullTable')), - text = 'Download') - ))) %>% - formatRound(columns=c('EEF_max_in.vitro_or_in.vivo', - 'AqT_EPA_acute', - 'AqT_EPA_chronic', - 'AqT_other_acute'), digits=3) - -``` - - -## SI Table 3: Intended Target Family - -```{r } - -intended_target <- select(endPointInfo, intended_target_family, intended_target_family_sub, endPoint = assay_component_endpoint_name, source = assay_source_long_name) %>% - right_join(select(filtered_ep, endPoint), by = "endPoint") %>% - arrange(intended_target_family, intended_target_family_sub) - -intended_target$intended_target_family_sub["Zebrafish" == intended_target$intended_target_family] <- "Zebrafish" - -intended_target <- intended_target %>% - rename(`Intended Target Family`=intended_target_family, - `Intended Target Family Sub-Family` = intended_target_family_sub) %>% - data.frame() - -datatable(intended_target, - rownames = FALSE, - extensions = 'Buttons', - options = list( - dom = 'Bfrtip', - buttons = list('colvis', list( - extend = 'collection', - buttons = list(list(extend='csv', - filename = 'siteTable'), - list(extend='excel', - filename = 'siteTable'), - list(extend='pdf', - filename= 'fullTable')), - text = 'Download') - ))) -# write.csv(intended_target, file="intended_target.csv", row.names = FALSE, na = "") -``` - - -Table 4: Endpoint Counts - -```{r } - -ACC <- ACC - - - -``` - - -## Table 5: Site Counts: - -```{r } - -# file_name <- "AOP_crosswalk.csv" -# full_path <- file.path(path_to_tox, file_name) -# -# AOP_crosswalk <- read.csv(full_path, stringsAsFactors = FALSE) -# -# AOP_crosswalk <- select(AOP_crosswalk, -# gene_symbol=Target.Gene.Symbol, -# AOP=AOP.name) -# -# AOP <- data.frame(gene_symbol = unique(AOP_crosswalk$gene_symbol), -# AOP = "", -# stringsAsFactors = FALSE) -# for(gene in AOP$gene_symbol){ -# AOP$AOP[AOP$gene_symbol %in% gene] <- paste(AOP_crosswalk$AOP[AOP_crosswalk$gene_symbol %in% gene],collapse = ", ") -# } - -chemicalSummary <- get_chemical_summary(tox_list = NULL, - ACClong, - filtered_ep, - chem.data = chem_data, - chem.site = chem_site, - chem.info = chem_info, - exclusion = exclusion) - -chemicalSummary <- chemicalSummary %>% - left_join(select(endPointInfo, - endPoint=assay_component_endpoint_name, - subFamily=intended_target_family_sub, - gene_symbol=intended_target_gene_symbol), by="endPoint") %>% - left_join(select(chem_info, CAS, `Chemical Name`), by="CAS") - -tableData <- chemicalSummary %>% - rename(Chemical=`Chemical Name`) %>% - group_by(site, endPoint, Family=Bio_category, subFamily, gene_symbol, date, Chemical) %>% - summarize(sumEAR = sum(EAR)) %>% - group_by(site, endPoint, Family, subFamily, gene_symbol, Chemical) %>% - summarize(maxEAR = max(sumEAR)) %>% - group_by(endPoint, Family, subFamily, gene_symbol, Chemical) %>% - summarize(nSites = sum(maxEAR > 10^-3)) %>% - data.frame() %>% - filter(nSites > 0) %>% - spread(Chemical, nSites) %>% - arrange(Family, subFamily, gene_symbol) %>% - select(endPoint, Family, subFamily, gene_symbol, everything()) - -tableData2 <- select(tableData, -endPoint, -Family, -subFamily, -gene_symbol) -tableData$nChems <- apply(tableData2, MARGIN = 1, function(x) sum(x>0, na.rm = TRUE)) - -# tableData <- tableData %>% -# left_join(AOP,by="gene_symbol") - -orderedCols <- data.frame(chnm = levels(chemicalSummary$chnm), - stringsAsFactors = FALSE) %>% - left_join(distinct(select(chemicalSummary, chnm, `Chemical Name`)), by = "chnm") - -orderedCols <- orderedCols$`Chemical Name`[which(orderedCols$`Chemical Name` %in% names(tableData))] - -tableData <- tableData[,c("Family", "subFamily","gene_symbol", - "endPoint", "nChems",rev(orderedCols))] - - -datatable(tableData, - rownames = FALSE, - extensions = 'Buttons', - options = list( - dom = 'Bfrtip', - buttons = list('colvis', list( - extend = 'collection', - buttons = list(list(extend='csv', - filename = 'siteTable'), - list(extend='excel', - filename = 'siteTable'), - list(extend='pdf', - filename= 'fullTable')), - text = 'Download') - ))) -# write.csv(tableData, file="wholeEnchilada.csv", row.names = FALSE, na = "") -``` diff --git a/vignettes/annotations.png b/vignettes/annotations.png index 4e30783c..8b92860f 100644 Binary files a/vignettes/annotations.png and b/vignettes/annotations.png differ diff --git a/vignettes/assays.png b/vignettes/assays.png index 7f150d63..11d5d8ed 100644 Binary files a/vignettes/assays.png and b/vignettes/assays.png differ diff --git a/vignettes/basicWorkflow.Rmd b/vignettes/basicWorkflow.Rmd new file mode 100644 index 00000000..1e99040e --- /dev/null +++ b/vignettes/basicWorkflow.Rmd @@ -0,0 +1,303 @@ +--- +title: "Basic Workflow" +date: "`r format(Sys.time(), '%d %B, %Y')`" +output: + rmarkdown::html_vignette: + toc: true + number_sections: false + fig_height: 7 + fig_width: 7 +vignette: > + %\VignetteEngine{knitr::rmarkdown} + %\VignetteIndexEntry{Basic Workflow} + \usepackage[utf8]{inputenc} +--- + +# Load Data + +Once the data is prepared as described in the vignette: "Prepare Data", it can be loaded into R using the `create_toxEval` function. + +```{r startup, message=FALSE} +library(toxEval) +path_to_tox <- system.file("extdata", package="toxEval") +file_name <- "OWC_data_fromSup.xlsx" +full_path <- file.path(path_to_tox, file_name) + +tox_list <- create_toxEval(full_path) + +``` + +The `create_toxEval` function will check that required columns are included, and give messages if there are unusual issues such as sites that don't match up with data, or chemicals that don't match up with chemical information. + +The vast majority of `toxEval` functions require a data frame described as `chemicalSummary`. The next section will walk through the options that are available in the workflow. + +# Setup Analysis + +The next step for exploring the data in `toxEval` is to create a data frame that includes the EAR calculations for each measurement. Since one chemical may have many more than one "endPoint", this data frame may be much larger then the "Data" tab in the Excel file. The general workflow for creating this data frame is: + +```{r chemicalSummary} +ACClong <- get_ACC(tox_list$chem_info$CAS) +ACClong <- remove_flags(ACClong) + +cleaned_ep <- clean_endPoint_info(endPointInfo) +filtered_ep <- filter_groups(cleaned_ep) + +chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) +``` + +What's going on with this workflow? + +## get_ACC + +The `get_ACC` function will retrieve the ACC values for the specified CAS (chemicals). The data was originally downloaded for `toxEval` on October 2015 from ToxCast. The data were combined from files in the "INVITRODB_V2_LEVEL5" folder. At the time of toxEval package release, this information was found +[here](https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data) in the "ToxCast & Tox21 Data Spreadsheet" data set. +The `ACC` data frame provided in `toxEval` is in a wide format. The function `get_ACC` will convert it to a long format, and convert the native log(uM) to ug/L. + +## remove_flags + +There are a set of flags that ToxCast includes with the ACC values. Depending on the scope of your analysis, you may want to include EARs with certain flags. EndPoints are removed based on their designated flag with the `remove_flags` function. The flags included in ToxCast, and the associated `flagsShort` value (used in the `remove_flags` function): + +| Flags | flagsShort | +|----------|------:| +| Borderline active | Borderline | +| Only highest conc above baseline, active | OnlyHighest | +| Only one conc above baseline, active | OneAbove | +| Noisy data | Noisy | +| Hit-call potentially confounded by overfitting | HitCall | +| Gain AC50 < lowest conc & loss AC50 < mean conc | GainAC50 | +| Biochemical assay with < 50% efficacy | Biochemical | + + +## clean_endPoint_info + +The `clean_endPoint_info` function cleans up the `endPointInfo` table from ToxCast. Filtering based on [Blackwell](https://pubs.acs.org/doi/10.1021/acs.est.7b01613). Specifically, this function hard-codes in the removal of endPoints that are ATG sources with signal loss, and NVS with signal gain. Also, this function adds some additional categories to intended_target_family and intended_target_family_sub as described in the paper linked above. + +## filter_groups + +This function takes the data frame from 'endPointInfo' and filters the endpoints in 3 steps. First, the user specifies the "groupCol" argument, which is a column header from `endPointInfo`. Second, the user specifies the assays to use. Finally, the user can also choose to remove specific group from the category. The default is to remove "Background Measurement" and "Undefined", but it is a good idea to check if other groups may not be relevant to the study. + +The default category ("groupCol") is "intended_target_family". Depending on the study, other categories may be more relavant. To discover to options, look at the column names in `endPointInfo`: + +```{r eval=FALSE} +names(endPointInfo) +``` + +By default, the BioSeek set of assays are removed. The list of assays and their short names are here: + +| Assay Name | Short Name | +|----------|------:| +|Apredica|APR| +|Attagene|ATG| +|BioSeek|BSK| +|NovaScreen|NVS| +|Odyssey Thera|OT| +|Toxicity Testing|TOX21| +|CEETOX|CEETOX| +|CLD|CLD| +|TANGUAY|TANGUAY| +|NHEERL_PADILLA|NHEERL_PADILLA| +|NCCT_SIMMONS|NCCT_SIMMONS| +|ACEA Biosciences|ACEA| + +## get_chemical_summary + +Finally, the function `get_chemical_summary` takes the measured user data from the output of `create_toxEval`, and joins the data with the endPoint information provided by ToxCast, as filtered by the functions described above. + +## Custom configuration + +Let's say we want to trim some names to make the labels more readable in graphs. Here we clean up some chemical class labels: + +```{r clean} +#Trim some names: +levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial" +levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent" +levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance" +``` + +And here, we convert the site lists to factors. This will make any site graph in an order that we define. Here, we take Great Lakes sites and make the order up-to-down stream: + +```{r sites} +#Ordering the sites to flow "downstream" of the Great Lakes: +sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", + "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", + "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", + "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", + "Manistee","Fox","Oconto","Peshtigo","Menominee", + "Indian","Cheboygan","Ford","Escanaba","Manistique", + "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", + "Clinton","Rouge","HuronMI","Raisin","Maumee", + "Portage","Sandusky","HuronOH","Vermilion","BlackOH", + "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", + "Genesee","Oswego","BlackNY","Oswegatchie","Grass", + "Raquette","StRegis") + + tox_list$chem_site$`Short Name` <- factor(tox_list$chem_site$`Short Name`, + levels = sitesOrdered) + +lakes_ordered <- c("Lake Superior", + "Lake Michigan", + "Lake Huron", + "Lake Erie", + "Lake Ontario") + +tox_list$chem_site$site_grouping <- factor(tox_list$chem_site$site_grouping, + levels=lakes_ordered) + +``` + +# Visualize Data + +## plot_tox_boxplots + +The `plot_tox_boxplots` function + +```{r boxplots1, warning=FALSE, message=FALSE} +library(grid) +plot_tox_boxplots(chemicalSummary, "Biological") +grid.text("# Sites w/ Detections:", + x = unit(.22, "npc"), + y = unit(.994, "npc"), gp=gpar(fontsize=7)) +# Other options: +# plot_tox_boxplots(chemicalSummary, "Chemical Class") +# plot_tox_boxplots(chemicalSummary, "Chemical") +``` + +The graph shows a slightly different result for a single site. First, let's set up a subset of data that we will use throughout this document to show a single site. We'll use the Maumee River data. + +```{r filtersiteBox, message=FALSE, warning=FALSE} +library(dplyr) + +maumee <- filter(chemicalSummary, shortName == "Maumee") +maumee_site <- filter(tox_list$chem_site, `Short Name` == "Maumee") +``` + + +Now, we'll run `plot_tox_boxplots` on that single site. The difference in the output is that instead of listing the number of sites, it lists the number of unique chemical/endpoint combinations used to create the box plot. We've also included +the code to add a custom title to the output: + +```{r maumeePlot, message=FALSE, warning=FALSE} +library(ggplot2) + +maumee_plot <- plot_tox_boxplots(maumee, "Biological") +maumee_plot <- maumee_plot + + ggtitle(maumee_site$Fullname[1]) +print(maumee_plot) +grid.text("# Unique Chemical/endPoints:", + x = unit(.225, "npc"), + y = unit(.962, "npc"), gp=gpar(fontsize=7)) +``` + +## plot_tox_boxplots + +```{r stackplots1, warning=FALSE, fig.width=10} +plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Biological") +grid.text("# Samples:", + x = unit(.03, "npc"), + y = unit(.205, "npc"), gp=gpar(fontsize=7)) +# More options: +# plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Chemical Class") +# plot_tox_stacks(chemicalSummary, tox_list$chem_site, "Chemical", include_legend = FALSE) +``` + +The graph shows a slightly different result for a single site: + +```{r siteStacks, message=FALSE, warning=FALSE, fig.width=10} +maumee_plot_stack <- plot_tox_stacks(maumee, maumee_site,"Biological") + +maumee_plot_stack <- maumee_plot_stack + + ggtitle(maumee_site$Fullname[1]) +print(maumee_plot_stack) + +``` + + +## plot_tox_heatmap + +```{r heat, warning=FALSE, fig.width=10} +plot_tox_heatmap(chemicalSummary, + tox_list$chem_site, + category = "Biological") +# More options: +# plot_tox_heatmap(chemicalSummary, tox_list$chem_site, category = "Chemical Class") +# plot_tox_heatmap(chemicalSummary, tox_list$chem_site, category = "Chemical") +``` + +## plot_tox_endpoints + +```{r endpoints, warning=FALSE} +plot_tox_endpoints(chemicalSummary, filterBy = "Cell Cycle") +grid.text("# Sites w/ Detections:", + x = unit(.38, "npc"), + y = unit(.995, "npc"), gp=gpar(fontsize=7)) +# More options: +# plot_tox_endpoints(chemicalSummary, category = "Chemical Class", filterBy = "PAHs") +# plot_tox_endpoints(chemicalSummary, category = "Chemical", filterBy = "Atrazine") +``` + +# Tables + +## table_tox_rank + +```{r table_tox_rank, warning=FALSE} +library(DT) +options(DT.options = list(pageLength = 5)) + +table_tox_rank(chemicalSummary, category = "Biological") +# More options: +# table_tox_rank(chemicalSummary, category = "Chemical Class") +# table_tox_rank(chemicalSummary, category = "Chemical") +``` + +The tables show slightly different results for a single site: + +```{r table_tox_rank_site, warning=FALSE} +table_tox_rank(maumee, category = "Biological") +``` + +## table_tox_sum + +```{r table_tox_sum, warning=FALSE} +table_tox_sum(chemicalSummary, category = "Biological") +# More options: +# table_tox_sum(chemicalSummary, category = "Chemical Class") +# table_tox_sum(chemicalSummary, category = "Chemical") +``` + +The tables show slightly different results for a single site: + +```{r table_tox_sum_site, warning=FALSE} +table_tox_sum(maumee, category = "Biological") +``` + + +## table_endpoint_hits + +```{r table_endpoint_hits, warning=FALSE} +table_endpoint_hits(chemicalSummary, category = "Biological") +# More options: +# table_endpoint_hits(chemicalSummary, category = "Chemical Class") +# table_endpoint_hits(chemicalSummary, category = "Chemical") +``` + +The tables show slightly different results for a single site. Instead of the number of sites with hits above a threshold, it is now the number of samples. + +```{r table_endpoint_hits_site, warning=FALSE} +table_endpoint_hits(maumee, category = "Biological") +``` + + +## table_tox_endpoint + +```{r table_tox_endpoint, warning=FALSE} +table_tox_endpoint(chemicalSummary, category = "Biological") +# More options: +# table_tox_endpoint(chemicalSummary, category = "Chemical Class") +# table_tox_endpoint(chemicalSummary, category = "Chemical") +``` + +The tables show slightly different results for a single site, showing the number of samples with hits (instead of number of sites). + +```{r table_tox_endpoint_site, warning=FALSE} +table_tox_endpoint(maumee, category = "Biological") +``` + diff --git a/vignettes/fig1.Rmd b/vignettes/fig1.Rmd deleted file mode 100644 index 9e12e7f9..00000000 --- a/vignettes/fig1.Rmd +++ /dev/null @@ -1,239 +0,0 @@ ---- -title: "Figure 1. Exposure activity ratios (EARs)" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{Figure_1} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 10, - fig.width = 7) -``` - -Exposure activity ratios (EARs) using ToxCast endpoints for screening of potential adverse biological impact in sample analysis results from 57 tributaries of the Great Lakes, 2010-2013 (A and B), toxicity quotients comparing established water quality criteria to sample results (C), and 17-beta estradiol equivalency quotients (ng E2/L) for compounds with established estradiol equivalency factors (D). Compounds are grouped by chemical class and ordered by largest to smallest median EAR. Chemicals where water quality guidelines could not be identified are indicated with “*”. E2, 17-β estradiol; n, number of sampling locations with detections of each chemical. [Boxes, 25th to 75th percentiles; dark line, median; whiskers, 1.5 x the interquartile range (IQR); circles, values outside 1.5 x the IQR. n, number of sites with detections of chemicals that have activity for endpoints in designated intended target families.] - - - -```{r } -library(readxl) -library(toxEval) -library(dplyr) -library(tidyr) -library(ggplot2) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" - -full_path <- file.path(path_to_tox, file_name) - -chem_data <- read_excel(full_path, sheet = "Data") -chem_info <- read_excel(full_path, sheet = "Chemicals") -chem_site <- read_excel(full_path, sheet = "Sites") -exclusion <- read_excel(full_path, sheet = "Exclude") - -#Trim names for graph: - -chem_info$Class[chem_info$Class == "Antimicrobial Disinfectants"] <- "Antimicrobial" -chem_info$Class[chem_info$Class == "Detergent Metabolites"] <- "Detergent" -chem_info$Class[chem_info$Class == "Flavors and Fragrances"] <- "Flavor/Fragrance" - -ACClong <- get_ACC(chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list = NULL, - ACClong, - filtered_ep, - chem.data = chem_data, - chem.site = chem_site, - chem.info = chem_info, - exclusion = exclusion) - - -# Make a "summary" of EAR's using Water Quality Guidelines instead of toxCast: -guideline_sum <- chem_info %>% - gather(endPoint, WQ_value, -CAS) %>% - filter(WQ_value != "-") %>% - mutate(WQ_value = as.numeric(WQ_value)) %>% - right_join(chem_data, by="CAS") - -WQ <- guideline_sum %>% - filter(endPoint %in% c("AqT_EPA_acute","AqT_EPA_chronic", - "AqT_other_chronic","AqT_other_acute")) %>% - mutate(EAR = Value/WQ_value) %>% - group_by(SiteID,`Sample Date`,CAS) %>% - summarise(sumEAR=sum(EAR)) %>% - data.frame() %>% - group_by(SiteID, CAS) %>% - summarise(maxEAR=max(sumEAR)) %>% - data.frame() %>% - mutate(guide_up = "Water Quality Guideline") %>% - mutate(guide_side = "Traditional") - -EEQ <- guideline_sum %>% - filter(endPoint %in% c("EEF_max_in.vitro_or_in.vivo")) %>% - mutate(EAR = Value*WQ_value*1000) %>% - group_by(SiteID,`Sample Date`,CAS) %>% - summarise(sumEAR=sum(EAR)) %>% - data.frame() %>% - group_by(SiteID, CAS) %>% - summarise(maxEAR=max(sumEAR)) %>% - data.frame() %>% - mutate(guide_up = "EEQ") %>% - mutate(guide_side = "Traditional") - -# Make a "summary" of EAR's using toxCast: - -toxCast <- chemicalSummary %>% - select(-Bio_category, -shortName, -chnm) %>% - group_by(site,date,CAS, Class) %>% - summarise(sumEAR=sum(EAR)) %>% - data.frame() %>% - group_by(site, CAS, Class) %>% - summarise(maxEAR=max(sumEAR)) %>% - data.frame() %>% - rename(SiteID = site) %>% - mutate(guide_side = "ToxCast") %>% - left_join(select(chem_info, CAS, `Chemical Name`), by="CAS") - -# We need extra rows for comparing tox with WQG and EEQ: -tox_WQ <- toxCast %>% - mutate(guide_up = "Water Quality Guideline") - -tox_EEQ <- filter(toxCast, CAS %in% unique(EEQ$CAS)) %>% - mutate(guide_up = "EEQ") - -cbValues <- c("#DCDA4B","#999999","#00FFFF","#CEA226","#CC79A7","#4E26CE", - "#FFFF00","#78C15A","#79AEAE","#FF0000","#00FF00","#B1611D", - "#FFA500","#F4426e") - -order_Class <- toxCast %>% - group_by(Class,`Chemical Name`) %>% - summarise(median = median(maxEAR[maxEAR != 0])) %>% - data.frame() %>% - arrange(desc(median)) %>% - filter(!duplicated(Class)) %>% - arrange(median) - -EEQ <- EEQ %>% - left_join(select(chem_info, CAS, `Chemical Name`, Class), by="CAS") -WQ <- WQ %>% - left_join(select(chem_info, CAS, `Chemical Name`, Class), by="CAS") - - -graphData <- bind_rows(tox_WQ, tox_EEQ, EEQ, WQ) - -#Primary ordering needs to be the highest -> lowest class, then order by median -orderChem <- bind_rows(tox_WQ, - filter(EEQ, !(CAS %in% unique(tox_WQ$CAS))), - filter(WQ, !(CAS %in% unique(tox_WQ$CAS)))) %>% - group_by(`Chemical Name`,Class) %>% - summarise(median = quantile(maxEAR[maxEAR != 0],0.5)) %>% - data.frame() %>% - mutate(Class = factor(Class, levels=order_Class$Class)) %>% - arrange(Class, !is.na(median), median) - -orderedLevels <- as.character(orderChem$Chemical.Name) - - -graphData <-graphData %>% - mutate(Class = factor(Class, levels=rev(order_Class$Class)), - `Chemical Name` = factor(`Chemical Name`, levels=orderedLevels), - guide_side = factor(guide_side), - guide_up = factor(guide_up, levels = c("Water Quality Guideline","EEQ"))) - -levels(graphData$guide_side) <- c("ToxCast\nMaximum EAR Per Site", - "Traditional\nMaximum Quotient Per Site") - -#Adding counts to the side: -countNonZero <- graphData %>% - select(SiteID, `Chemical Name`,guide_side,guide_up, maxEAR) %>% - group_by(SiteID, `Chemical Name`,guide_side,guide_up) %>% - summarise(meanEAR = mean(maxEAR, na.rm=TRUE)) %>% - group_by(`Chemical Name`,guide_side,guide_up) %>% - summarise(nonZero = as.character(sum(meanEAR>0))) %>% - data.frame() %>% - select(Chemical.Name, guide_up, nonZero) %>% - distinct() %>% - mutate(guide_side = factor("ToxCast\nMaximum EAR Per Site", - levels = levels(graphData$guide_side)), - guide_up = factor(guide_up, levels = levels(graphData$guide_up)), - `Chemical Name` = factor(Chemical.Name, - levels = levels(graphData$`Chemical Name`))) - -# WQ: Astricts to chemicals with no endpoints: -astrictData_WQ <- countNonZero %>% - mutate(guide_side = factor("Traditional\nMaximum Quotient Per Site", - levels = levels(graphData$guide_side))) %>% - filter(guide_up == "Water Quality Guideline") %>% - mutate(nonZero = "*") %>% - filter(!(`Chemical Name` %in% unique(WQ$`Chemical Name`))) - -# EEQ: Astricts to chemicals with no endpoints: -astrictData_EEQ <- countNonZero %>% - mutate(guide_side = factor("ToxCast\nMaximum EAR Per Site", - levels = levels(graphData$guide_side))) %>% - filter(guide_up == "EEQ") %>% - mutate(nonZero = "*") %>% - filter(!(`Chemical Name` %in% unique(tox_EEQ$`Chemical Name`))) - -# Label upper right corner for each facet (probably an easier way...): -textData <- select(graphData, guide_up, guide_side) %>% - distinct() %>% - mutate(textExplain = c("A","B","C","D"), - y = c(10,10,100,100), - `Chemical Name` = factor(rep("4-Nonylphenol (sum of all isomers)",4), levels = levels(graphData$`Chemical Name`))) - -toxPlot_All <- ggplot(data=graphData) + - scale_y_log10(labels=fancyNumbers) + - geom_boxplot(aes(x=`Chemical Name`, y=maxEAR, fill=Class), - lwd=0.1,outlier.size=1) + - facet_grid(guide_up ~ guide_side, scales = "free", space = "free") + - theme_bw() + - scale_x_discrete(drop=TRUE) + - coord_flip() + - theme(axis.text = element_text( color = "black"), - axis.text.y = element_text(size=7), - axis.title=element_blank(), - panel.background = element_blank(), - plot.background = element_rect(fill = "transparent",colour = NA), - strip.background = element_rect(fill = "transparent",colour = NA), - strip.text.y = element_blank()) + - guides(fill=guide_legend(ncol=6)) + - theme(legend.position="bottom", - legend.justification = "left", - legend.background = element_rect(fill = "transparent", colour = "transparent"), - legend.title=element_blank(), - legend.text = element_text(size=8), - legend.key.height = unit(1,"line")) + - scale_fill_manual(values = cbValues, drop=FALSE) - -ymin <- 10^-6 -ymax <- ggplot_build(toxPlot_All)$layout$panel_ranges[[1]]$y.range[2] - -toxPlot_All_withLabels <- toxPlot_All + - geom_text(data=countNonZero, aes(x= `Chemical Name`, label = nonZero, y=ymin), size=2.5) + - geom_text(data = textData, aes(x=`Chemical Name`, label=textExplain, y=y), - size = 3) + - geom_text(data = astrictData_WQ, aes(x=`Chemical Name`, label=nonZero, y=10^-5), - size=5, vjust = 0.70) + - geom_text(data = astrictData_EEQ, aes(x=`Chemical Name`, label=nonZero, y=3.3*ymin), - size=5, vjust = 0.70) - -toxPlot_All_withLabels - -``` \ No newline at end of file diff --git a/vignettes/fig2.Rmd b/vignettes/fig2.Rmd deleted file mode 100644 index 87d1b1e3..00000000 --- a/vignettes/fig2.Rmd +++ /dev/null @@ -1,110 +0,0 @@ ---- -title: "Figure 2. Number of individual chemicals" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{Figure_2} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 9) -``` - - -Number of individual chemicals with at least one sample that resulted in an exposure activity ratio > 10-3 for each site. - - - -```{r } -library(toxEval) -library(dplyr) -library(ggplot2) -library(grid) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -#Ordering the sites to flow "downstream" of the Great Lakes: -sitesOrdered <- c("StLouis","Nemadji","WhiteWI","Bad","Montreal", - "PresqueIsle","Ontonagon","Sturgeon","Tahquamenon","Burns", - "IndianaHC","StJoseph","PawPaw","Kalamazoo","GrandMI", - "Milwaukee","Muskegon","WhiteMI","PereMarquette","Manitowoc", - "Manistee","Fox","Oconto","Peshtigo","Menominee", - "Indian","Cheboygan","Ford","Escanaba","Manistique", - "ThunderBay","AuSable","Rifle","Saginaw","BlackMI", - "Clinton","Rouge","HuronMI","Raisin","Maumee", - "Portage","Sandusky","HuronOH","Vermilion","BlackOH", - "Rocky","Cuyahoga","GrandOH","Cattaraugus","Tonawanda", - "Genesee","Oswego","BlackNY","Oswegatchie","Grass", - "Raquette","StRegis") - - -threshold <- 10^-3 - -graphData <- chemicalSummary %>% - group_by(site, date, chnm) %>% - summarize(sumEAR = sum(EAR)) %>% - group_by(site, chnm) %>% - summarize(maxEAR = max(sumEAR), - count = n()) %>% - group_by(site, count) %>% - summarize(nChem = sum(maxEAR > threshold)) %>% - data.frame() %>% - left_join(select(tox_list[["chem_site"]], site=SiteID, `Short Name`, site_grouping), - by = "site") %>% - mutate(`Short Name` = factor(`Short Name`, levels = sitesOrdered)) %>% - mutate(site_grouping = factor(site_grouping, levels = c("Lake Superior", - "Lake Michigan", - "Lake Huron", - "Lake Erie", - "Lake Ontario"))) - -countPlot <- ggplot(graphData, aes(x=`Short Name`))+ - geom_bar(aes(y=nChem), - stat = "identity", - fill = "steelblue") + - geom_text(aes(y=-1, label = count), size = 2.5) + - theme_bw() + - facet_grid(. ~ site_grouping, scales="free", space="free") + - xlab("") + - ylab("Number of Chemicals\n with EARmax > 0.001") + - theme(axis.text.x = element_text( angle = 90,vjust=0.5,hjust = 1)) + - theme(strip.text.y = element_text(angle=0, hjust=0, size=7), - strip.text.x = element_text(size = 8), - strip.background = element_rect(fill="transparent", colour = NA), - axis.text = element_text(size=8), - panel.spacing = unit(0.05, "lines"), - panel.grid.major = element_blank(), - panel.grid.minor = element_blank(), - plot.background = element_rect(fill = "transparent",colour = NA)) - -countPlot -grid.text("# Samples:", - x = unit(.04, "npc"), - y = unit(.19, "npc"), gp=gpar(fontsize=7)) -``` - - diff --git a/vignettes/fig3.Rmd b/vignettes/fig3.Rmd deleted file mode 100644 index c32904ec..00000000 --- a/vignettes/fig3.Rmd +++ /dev/null @@ -1,85 +0,0 @@ ---- -title: "Figure 3: Biological influence" -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - rmarkdown::html_vignette -vignette: > - %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{Figure_3} - \usepackage[utf8]{inputenc} ---- - - -```{r setup, include=FALSE} -library(knitr) -library(rmarkdown) -options(continue=" ") -options(width=60) -knitr::opts_chunk$set(echo = TRUE, - warning = FALSE, - message = FALSE, - fig.height = 7, - fig.width = 7) -``` - -## Biological influence - -Boxplots of the maximum summation of exposure activity ratios for each site by individual intended target families computed using individual water samples collected from 57 Great Lakes Tributaries from 2010-2013. Boxplots include only those sites that had detections for chemicals that influence the intended target families in ToxCast assays. Chemicals that influence the intended target family transferase were not detected. None of the monitored chemicals influence endpoints from the intended target families phosphatase, esterase, hydrolase, lyase, methyltransferase, and miscellaneous protein (all not included in this figure). [Boxes, 25th to 75th percentiles; dark line, median; whiskers, 1.5 x the interquartile range (IQR); circles, values outside 1.5 x the IQR. n, number of sites with detections of chemicals that have activity for endpoints in designated intended target families.] - - - -```{r } -library(toxEval) -library(grid) - -path_to_tox <- system.file("extdata", package="toxEval") -file_name <- "OWC_data_fromSup.xlsx" -full_path <- file.path(path_to_tox, file_name) - -tox_list <- create_toxEval(full_path) - -ACClong <- get_ACC(tox_list$chem_info$CAS) -ACClong <- remove_flags(ACClong) - -cleaned_ep <- clean_endPoint_info(endPointInfo) -filtered_ep <- filter_groups(cleaned_ep) - -chemicalSummary <- get_chemical_summary(tox_list, ACClong, filtered_ep) - -#Trim some names: -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Antimicrobial Disinfectants"] <- "Antimicrobial" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Detergent Metabolites"] <- "Detergent" -levels(chemicalSummary$Class)[levels(chemicalSummary$Class) == "Flavors and Fragrances"] <- "Flavor/Fragrance" - -bioPlot <- plot_tox_boxplots(chemicalSummary, - category = "Biological", - manual_remove = c("Transferase","Undefined")) - -bioPlot -grid.text("# Samples", - x = unit(.22, "npc"), - y = unit(.995, "npc"), gp=gpar(fontsize=7)) -``` - -Could also do chemical classes: - -```{r } -classPlot <- plot_tox_boxplots(chemicalSummary, - category = "Chemical Class") -classPlot -grid.text("# Samples", - x = unit(.22, "npc"), - y = unit(.995, "npc"), gp=gpar(fontsize=7)) -``` - -Could also or chemicals: - -```{r } - -chemPlot <- plot_tox_boxplots(chemicalSummary, - category = "Chemical") -chemPlot -grid.text("# Samples", - x = unit(.35, "npc"), - y = unit(.995, "npc"), gp=gpar(fontsize=7)) -``` \ No newline at end of file diff --git a/vignettes/flags.png b/vignettes/flags.png index e6256cc7..fa29509c 100644 Binary files a/vignettes/flags.png and b/vignettes/flags.png differ diff --git a/vignettes/group.png b/vignettes/group.png index 5c067a23..19f350ae 100644 Binary files a/vignettes/group.png and b/vignettes/group.png differ diff --git a/vignettes/main.png b/vignettes/main.png index 97df5948..b8ccf9ad 100644 Binary files a/vignettes/main.png and b/vignettes/main.png differ diff --git a/vignettes/shinyApp.Rmd b/vignettes/shinyApp.Rmd index 5eadd8e7..fb2d50e1 100644 --- a/vignettes/shinyApp.Rmd +++ b/vignettes/shinyApp.Rmd @@ -1,16 +1,15 @@ --- -title: "Preparing toxEval Analysis" +title: "Shiny App" date: "`r format(Sys.time(), '%d %B, %Y')`" output: rmarkdown::html_vignette: toc: true - number_sections: true - fig_caption: yes + number_sections: false fig_height: 10 fig_width: 7 vignette: > %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{Figure_1} + %\VignetteIndexEntry{Shiny App} \usepackage[utf8]{inputenc} --- @@ -25,104 +24,96 @@ knitr::opts_chunk$set(echo = TRUE, message = FALSE) ``` -The `toxEval` R-package includes a set of functions to analyze, visualize, and organize measured concentration data as it relates to ToxCast data. These functions allow great flexibly for exploring the potential biological affects of measured chemicals. Also included in the package is a browser-based application made from the `shiny` R-package. There is less customization from the application, but it is a convenient way to explore the data. -This vignette describes the process to prepare the data and use the shiny application. There is also a section on installing and updating the package, since it is not on CRAN. - -# Preparing the data - -The data should be prepared in a Microsoft\textregistered\ Excel file. There are 3 mandatory sheets (also known as tabs), and 2 optional sheets. The sheets should appear like this (although the order is not important): - - - -Each sheet has mandatory columns; the order of the columns is not important, but the names of the columns is important. Additional columns can be included but will be ignored. The top row of each sheet should contain the column names (headers), no rows should be initially skipped, and no titles or comment rows should precede the data. - -## Data +# Using the Shiny Application -The "Data" tab needs to have at a minimum 4 columns: "CAS", "SiteID", "Value", and "Sample Date". They can be in any order, but the first row of sheet should be the header (column names). +All of the output on the Shiny app can be created directly in R as well. Using the app is a quick and convenient way to explore your data. For more customization, the R-code to produce each graph and table is displayed in the app. That is a good starting-point for a custom analysis. See the help file for any functions on details and example workflows. -* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose "Format Cells", and choose "Text" as the category to assure they retain their format. The unique CAS values in this column *must* match with the CAS values in the "Chemicals" sheet. +To run the app, load the `toxEval` package, and run the `explore_endpoints` function: -* SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique "SiteID" values *must* match the "SiteID" values in the "Sites" sheet. There is no requirements on formatting for this column. +```{r runApp, eval=FALSE} +library(toxEval) +explore_endpoints() +``` -* Value: This column is numeric. It should be the concentration of the chemical in $\mu$g/L. +Your default browser will open up and display the following page: -* Sample Date: Currently this column only serves to identify unique samples. Dates and times as formatted in Excel, as well as integer values are acceptable. The actual date and times are not currently used to perform any time series analysis. + -Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis. +Generally the app can be divided into 3 main sections: -As an example, the first several rows of a minimal example would look like this: +The left sidebar (with the black background) will be used to load the data and set up the conditions for analysis. The options here will be described in the next section. - +The top main section includes a variety of tabs to choose from to explore the data in different ways. Each section will be described in the section "Main Output". -## Chemicals +The bottom section "R Code:" shows the exact code you could use to run directly in R to reproduce the analysis. The code can be copied and pasted directly into the R console, or (preferred for reproducible!) saved in an R script. One important note is that the local path to the file is not recorded, so if your data file is not located in your current working directory, you'll need to add the path to your file name. -The "Chemicals" tab needs to have at a minimum 2 columns: "CAS" and "Class". They can be in any order, but the first row of sheet should be the header (column names). +## Sidebar options -* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. In the Excel file, you may need to pay special attention that no CAS are converted to a Date format. Highlight the column, right-click on the mouse, choose "Format Cells", and choose "Text" as the category to assure they retain their format. The unique CAS values in this column *must* match with the CAS values in the "Data" sheet. +
-* Class: A character column defining the class of chemicals. Most `toxEval` functions will allow groupings by either chemical, class (as defined here), or biological grouping (will be discussed in section TODO:XXXXXXXX)
+The sidebar offers a host of options for customizing the analysis. This document will attempt to describe the logistics of options. This document will NOT go into the scientific details of what options to choose. That will need to be up to the user.
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
-## Sites
+### A. Load Excel File
-The "Sites" tab needs to have at a minimum 4 columns: "SiteID", "Short Name", "dec_lon", and "dec_lat". They can be in any order, but the first row of sheet should be the header (column names).
+Once the Shiny App has been started, the first step is to load the data. The data should be prepared as described in the "Prepare Data" vignette. Click on the "Browse" button and navigate to the properly prepared Excel file. Once a file is picked, the data will be loaded into the application. There is a progress bar while the data loads, but sometimes it may take a little more time to view the data as each individual graph/map/table also takes some time to compute.
-* SiteID: This character column identifies the sites (or sampling locations) of the measurement. The unique "SiteID" values *must* match the "SiteID" values in the "Data" sheet. There is no requirements on formatting for this column.
+### B. 'Group By' option
-* Short Name: This character column is the name associated with the SiteID that will be used in the graphs and tables. The names should be sufficiently descriptive, but as short as possible to optimize the area on the graph devoted to the data and not the labels.
+The "Group By" radio buttons affect all output in the application. For each function, we will be summarizing the data by summing individual EARs. How we group those summations depends on this choice.
-* dec_lon: This numeric column is the decimal longitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most `toxEval` functions will not require this column.
+The "Group" choice will group all of the endPoints that have similar groupings based on the options as determined by the designated "Annotation". The default annotation is "intended_target_family". With that choice, the "Group" radio button groups all EARs by the unique categories in the "intended_target_family" category.
-* dec_lat: This numeric column is the decimal latitude of the sampling location. It is mainly used for plotting the sampling locations on a map in the Shiny app. Most `toxEval` functions will not require this column.
+The "Chemical" choice will group all of the endPoints in a single chemical (as defined by unique CAS).
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
+The "Class" choice will group all of the endpoints in all of the chemicals as defined in the "Chemical" tab in the Excel file.
-## Exclude
+### C. Mean and Max: Summarize by site
-The "Exclude" tab is an optional tab. If used, the "Exclude" tab needs to have at a minimum 2 columns: "CAS" and "endPoint". They can be in any order, but the first row of sheet should be the header (column names).
+The options are "MeanEAR" and "MaxEAR", and they dictate how to summarize the EARs per site. Each site may have different numbers of collected samples, and depending on the scope of your study, it may be more important to analyze the mean of the site, or look at the maximum at each site.
-* endPoint: A character column define a specific ToxCast endpoint to ignore. IF the "CAS" in the corresponding row is empty, the endPoint will be completely excluded from the `toxEval` analysis. For example, maybe it was decided that TOX21_p53_BLA_p3_ratio was not an appropriate endPoint to consider in the analysis. We can exclude all TOX21_p53_BLA_p3_ratio endPoints by adding it to the Exclude tab. IF the "CAS" in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
+### D. Download benchmarks
-* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry. IF the "endPoint" in the corresponding row is empty, the chemical will be completely excluded from the `toxEval` analysis. For example, maybe it was discovered that DEET was contaminated in the sampling method. We can exclude all DEET measurements by adding 134-62-3 to the Exclude tab. IF the "endPoint" in the corresponding row is NOT empty, the specific chemical/endPoint combination will be excluded from the analysis.
+Clicking on this button will download the a comma delimited (csv) file that could be used in the "Benchmark" tab (as described in the "Prepare Data" vignette). The provided columns are "CAS", "Chemical" (name), "flags", "endPoint", "ACC", "MlWt", "Value", "groupCol", and "assaysFull".
-There could be a variety of reasons to exclude chemical/endpoint combinations. This will be discussed further in section TODO: XXXXXXXXX.
+The "ACC" column is in log(uM), and the "Value" column is the conversion from that log(uM) to ug/L.
+
+### E. Assay
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
+
-## Benchmarks
+The assays dropdown menu offers the option to remove certain assays from the analysis. By default, the "BioSeek" assay is not included. Clicking on any of the options will not have an immediate affect until the "Switch Assays" button is clicked.
-The "Benchmarks" tab is an optional tab. The benchmark tab can be used to over-ride the ToxCast endpoints. For example, there may be a need to perform similar `ToxEval` analysis using EPA benchmarks instead of ToxCast endpoints.
+### F. Annotations
-For the functions to work seamlessly with all `toxEval` functions, the "Benchmarks" tab needs to have at a minimum 5 columns: "CAS", "Chemical", "endPoint", "Value", and "groupCol". They can be in any order, but the first row of sheet should be the header (column names).
+The "Annotations" dropdown offers the choice of which ToxCast grouping to analyze. By default, the "intended_target_family" is picked, but there may be other more appropriate options based on the scope of the analysis. Choosing a new annotation will not have an immediate affect until the "Switch Annotation" button is clicked.
-* CAS: A character column defining the chemicals via their Chemical Abstracts Service (CAS) registry.
+### G. Group
-* Chemical: A character column defining the name of the chemicals.
+The options in the "Group" dropdown change depending on the choice in Annotations. For "intended_target_family" for example, there would be the choices such as "Nuclear Receptors", "Cell Cycle", "DNA Binding", etc. You can ignore certain groups ("Background Measurement" for instance) by leaving the options unchecked. Clicking on and off the checkboxes will not have an immediate affect until the "Switch Groups" button is clicked.
-* endPoint: A character column naming the benchmark
+### H. Flags
-* Value: The concentration (in identical units as what is reported in the "Data" tab) of the benchmark.
+
-* groupCol: A character column that groups endpoints. This is analogous to the Biological groupings from the ToxCast analysis.
+The choices in the "Flags" dropdown are which flags to include in your analysis. By default, endPoints flagged with "Noisy Data", "Only one conc above baseline, active", and "Hit-call potentially confounded by overfitting" are included in the analysis. The scope of the analysis may cause you to want to include more or less flagged endPoints. Clicking on the flag checkboxes will not have an immediate affect until the "Switch Flags" button is clicked.
-Note: Additional columns may be useful to organize the data. They will not hinder a `toxEval` analysis.
+### I. Sites
-# Checking the data
+By default, the analysis is done on all the sites. However, you can dig down to an individual sites using this dropdown menu. Each graph and table may show the individual site data a bit different than the full data set analysis, and that is described in the "Basic Workflow" vignette
-Loading the Excel
+### J. Hit Threshold
-## Alternative method
+There are a few outputs (from the tabs: Hit Count, Site Hits, and Endpoint Hits) that will calculate the number of "Hits"...which is simply the number of times the EAR is above a designated threshold. That threshold value is defined here. Changing the threshold will not have an immedate affect until the "Change Hit Threshold" button is clicked.
+## Main Output
-# Using the Shiny Application
+Each plot and table offered in the app is described in the "Basic Workflow" vignette. Please refer there for more information.
-## Loading data
+For each plot, there is an option to download the plot or data that is in the plot. The R-code to generate the output is also provided under each output. This would be the ideal way to customize any additional output.
-## Sidebar
-
-# Getting Started
+# Package logistics
## Installation of R and RStudio
@@ -163,9 +154,10 @@ install.packages("toxEval")
## Update toxEval
+
+
Regularly, it is a good idea to update *ALL* your packages in R. If using RStudio, this is quite easy, there's an Update button in the "Packages" tab. This checks CRAN and GRAN for updates. It is a good idea to click this update regularly.
-
Within the R-console, packages can be update with the command:
```{r eval=FALSE}
diff --git a/vignettes/sidebar.png b/vignettes/sidebar.png
index f5e5cfc6..3c772a81 100644
Binary files a/vignettes/sidebar.png and b/vignettes/sidebar.png differ
diff --git a/vignettes/sites.png b/vignettes/sites.png
index db9ee0c9..dce7e159 100644
Binary files a/vignettes/sites.png and b/vignettes/sites.png differ