This is the official implementation for drGAT: Attention-Guided Gene Assessment for Drug Response in Drug-Cell-Gene Heterogeneous Network.
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.
This quick start guide demonstrates how to run drGAT predictions on both CPU and GPU, completing the process within seconds.
git clone git@github.com:inoue0426/drGAT.git
cd drGAT
docker pull inoue0426/drgat
docker run -it -p 9999:9999 inoue0426/drgatThen access to http://localhost:9999/notebooks/Tutorial.ipynb and run all cells.
For re-training the model, refer to model_training.ipynb. If you want to use your dataset, create_dataset.ipynb might be useful.
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
** NOTE: Please ensure the version matches exactly with your GPU/CPU specifications.
Our experiment was conducted on Ubuntu with an NVIDIA A100 Tensor Core GPU.
If you want to re-train model, we recommend using GPU.
git clone git@github.com:inoue0426/drGAT.git
cd drGAT
conda env create -f environment.yml
conda activate drGAT
python -m ipykernel install --user --name=drGAT
jupyter notebook --port=9999Then access to http://localhost:9999/notebooks/Tutorial.ipynb
git clone git@github.com: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
python -m ipykernel install --user --name=drGAT
jupyter notebook --port=9999** NOTE: Please ensure the version matches exactly with your GPU/CPU specifications.
@article{inoue2024drgat,
title={drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network},
author={Inoue, Yoshitaka and Lee, Hunmin and Fu, Tianfan and Luna, Augustin},
journal={ArXiv},
year={2024},
publisher={arXiv}
}