BiG-DRP: Bipartite Graph-based Drug Response Predictor

IMPROVE PROJECT INSTRUCTIONS

The improve project requires standarized interfaces for data preprocessing, training and inference

Requirements

conda>=23.5

IMPROVE PROJECT INSTRUCTIONS

The improve project IMPROVE Projectrequires standarized interfaces for data preprocessing, training and inference, follow the code for BiG-DRP in BiG-DRP

#Installation

The IMPROVE project is currently using the develop branch

Using Conda

Create environment

conda env create -f Big-DRP_conda.yml

Activate the environment

conda activate python_BigDRP

Download BiG-DRP

git clone -b develop git@github.com:JDACS4C-IMPROVE/BiG-DRP.git
cd BiG-DRP

Install Torch for CUDA. dgl and CANDLE package

python3 -m pip install --pre dgl -f https://data.dgl.ai/wheels/cu113/repo.html
python3 -m pip install --pre dglgo -f https://data.dgl.ai/wheels-test/repo.html
python3 -m pip install git+https://github.com/ECP-CANDLE/candle_lib@develop

Example usuage without container (running BiG-DRP)*

Preprocess (optional)

bash preprocess.sh  $CUDA_VISIBLE_DEVICES $CANDLE_DATA_DIR

Training

bash train.sh $CUDA_VISIBLE_DEVICES $CANDLE_DATA_DIR

Testing

bash infer.sh $CUDA_VISIBLE_DEVICES $CANDLE_DATA_DIR

Using pip [RECOMMENDED]

pip install --upgrade pip
python3 -m pip install torch==1.11.0+cu102 torchvision==0.12.0+cu102 torchaudio==0.11.0 torchmetrics==0.11.1 --extra-index-url https://download.pytorch.org/whl/cu102
python3 -m pip install --pre dgl -f https://data.dgl.ai/wheels/cu113/repo.html
python3 -m pip install --pre dglgo -f https://data.dgl.ai/wheels-test/repo.html
python3 -m pip install git+https://github.com/ECP-CANDLE/candle_lib@develop
cd BiG-DRP
python3 -m pip install -r requirements.txt
chmod a+x *.sh
chmod a+x *.py
sh train.sh 1 data

Example usage with container

Model definition file 'BiG_DRP.def' is located in here

git clone -b develop https://github.com/JDACS4C-IMPROVE/Singularity.git
cd Singularity

Build Singularity

singularity build --fakeroot BiG_DRP.def definitions/BiG_DRP.def

Execute with container

singularity exec --nv BiG_DRP.sif train.sh $CUDA_VISIBLE_DEVICES $CANDLE_DATA_DIR

DATA PREPROCESSING

To create the data run the preprocess.sh code to download the data. To use a custom dataset, set the 'improve_analysis" flag to 'yes' in the BiG_DRP_model.txt file

Model Training

1. train.sh $CUDA_VISIBLE_DEVICES $CANDLE_DATA_DIR

CANDLE_DATA_DIR=<PATH OF REPO/Data/>

Note: The train.sh script will download the original authors data if the Data directory is empty

  * set CUDA_VISIBLE_DEVICES to a GPU device ID to make this devices visible to the application.
  * CANDLE_DATA_DIR, path to base CANDLE directory for model input and outputs.
  * CANDLE_CONFIG , path to CANDLE config file must be inside CANDLE_DATA_DIR.

Example

• git clone ....
• cd BiG-DRP
• check permissions if all scripts are executable
• ./preprocess.sh 2 $CANDLE_DATA_DIR
• ./train.sh 2 $CANDLE_DATA_DIR
• ./infer.sh 2 $CANDLE_DATA_DIR

REQUIREMENTS

• pandas==1.1.2
• six==1.15.0
• scipy==1.6.2
• tqdm==4.60.0
• nose==1.3.7
• numpy==1.18.5
• scikit_learn==0.24.2
• json-encoder==0.4.4
• kiwisolver==1.4.5