Update README.md

README.md

@@ -8,32 +8,6 @@ This is the official implementation for **drGAT: Attention-Guided Gene Assessmen
 
 This model is created to understand how genes influence Drug Response using Graph Attention Networks (GAT) on heterogeneous networks of drugs, cells, and genes. It predicts Drug Response based on the attention coefficients generated during this process. This has been implemented in Python.
 
-```
-@misc{inoue2024drgat,
-      title={drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network}, 
-      author={Yoshitaka Inoue and Hunmin Lee and Tianfan Fu and Augustin Luna},
-      year={2024},
-      eprint={2405.08979},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
-}
-```
-
-## Requirement
-
-```
-numpy==1.23.5
-pandas==2.0.3
-matplotlib==3.7.1
-optuna==3.2.0
-torch==1.13.1+cu116
-torch-cluster==1.6.1+pt113cu116
-torch-geometric==2.3.1
-torch-scatter==2.1.1+pt113cu116
-torch-sparse==0.6.17+pt113cu116
-torch-spline-conv==1.2.2+pt113cu116
-```
-
 ## Environment
 
 Our experiment was conducted on Ubuntu with an NVIDIA A100 Tensor Core GPU.  
@@ -44,50 +18,11 @@ If you want to re-train model, we reccomend to use GPU.
 ```shell
 git clone [email protected]:inoue0426/drGAT.git
 cd drGAT
-docker build -t drgat:latest .
-docker run -it -p 9999:9999 drgat:latest
+docker pull inoue0426/drgat
+docker run -it -p 9999:9999 inoue0426/drgat
 ```
 
 Then access to http://localhost:9999/notebooks/Tutorial.ipynb 
 
-## Installation using Conda
-
-```shell
-git clone [email protected]:inoue0426/drGAT.git
-cd drGAT
-conda env create -f environment.yml
-conda activate drGAT
-```
-** NOTE: Please ensure the version matches exactly with your GPU/CPU specifications.
-
-
-## Installation using requirement.txt
-
-```shell
-git clone [email protected]:inoue0426/drGAT.git
-cd drGAT
-conda create --name drGAT python=3.10 -y
-conda activate drGAT
-pip install -r requirement.txt
-# Please make sure to change the version to match the version of your GPU/CPU machine exactly.
-pip install --no-cache-dir  torch==1.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
-pip install --no-cache-dir torch_geometric
-pip install --no-cache-dir pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.1%2Bcu116.html
-```
-** NOTE: Please ensure the version matches exactly with your GPU/CPU specifications.
-
-## Usage
-
-We offer a sample notebook. [Tutorial](https://github.com/inoue0426/drGAT/blob/main/Tutorial.ipynb)  
-If you want to try the model evaluation, you can skip the Train model section.
-
-After preprocessing, you can use our model as follows:
-
-```python
-model = torch.load('sample.pt')
-model = model.to(device)
-drGAT.eval(model, data)
-```
-
 ## Help
 If you have any questions or require assistance using MAGIC, please feel free to make issues on https://github.com/inoue0426/drGAT/