Online version: OmicLearn
π° Manual and Documentation is available at: OmicLearn ReadTheDocs Page
Transparent exploration of machine learning for biomarker discovery from proteomics and omics data. This is a maintained fork from OmicEra.
A three minute quickstart video to showcase OmicLearn can be found here.
Citation:
Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Furkan M Torun, Sebastian Virreira Winter, Sophia Doll, Felix M Riese, Artem Vorobyev, Johannes B MΓΌller-Reif, Philipp E Geyer, Maximilian T Strauss
bioRxiv 2021.03.05.434053; doi: https://doi.org/10.1101/2021.03.05.434053
π’ Streamlit share
This is an online version hosted by streamlit using free cloud resources, which might have limited performance. Use the local installation to run OmicLearn on your own hardware.
You can use the one-click installer to install OmicLearn as an application locally. Click on one of the links below to download the latest release for:
For detailed installation instructions of the one-click installers refer to the documentation.
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It is strongly recommended to install OmicLearn in its own environment using Anaconda or Miniconda.
- Redirect to the folder of choice and clone the repository:
git clone https://github.com/MannLabs/OmicLearn
- Create a new environment for OmicLearn:
conda create --name omic_learn python=3.9
- Activate the environment with
conda activate omic_learn
- Install OmicLearn with
cd OmicLearn & pip install .
- Redirect to the folder of choice and clone the repository:
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After a successful installation, type the following command to run OmicLearn:
python -m omiclearn
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After starting the streamlit server, the OmicLearn page should be automatically opened in your browser (Default link:
http://localhost:8501
The following image displays the main steps of OmicLearn:
Detailed instructions on how to get started with OmicLearn can be found here.
On this page, you can click on the titles listed in the Table of Contents, which contain instructions for each section.
All contributions are welcome. π
π° To get started, please check out our CONTRIBUTING
guidelines.
When contributing to OmicLearn, please open a new issue to report the bug or discuss the changes you plan before sending a PR (pull request).
We appreciate community contributions to the repository.