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.
@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}
}
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
Our experiment was conducted on Ubuntu with an NVIDIA A100 Tensor Core GPU.
If you want to re-train model, we reccomend to use GPU.
git clone git@github.com:inoue0426/drGAT.git
cd drGAT
docker build -t drgat:latest .
docker run -it -p 9999:9999 drgat:latestThen access to http://localhost:9999/notebooks/Tutorial.ipynb
git clone git@github.com: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.
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** NOTE: Please ensure the version matches exactly with your GPU/CPU specifications.
We offer a sample notebook. Tutorial
If you want to try the model evaluation, you can skip the Train model section.
After preprocessing, you can use our model as follows:
model = torch.load('sample.pt')
model = model.to(device)
drGAT.eval(model, data)If you have any questions or require assistance using MAGIC, please feel free to make issues on https://github.com/inoue0426/drGAT/