Using Interpretable Deep Learning to Model Cancer Dependencies

The code repository of Using Interpretable Deep Learning to Model Cancer Dependencies. C.H. Lin, O. Lichtarge Bioinformatics, 2021. If you have any questions or comments, feel free to contact Jack Chih-Hsu Lin (lin.chihhsu[at]gmail[dot]com).

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Content

• Download code
• Installation and download data
• Run experiments
• Project organization

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Download code

git clone https://github.com/LichtargeLab/BioVNN.git

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Installation and download data

Requirements

• Anaconda or MiniConda
• GPU >= 3GB
• Python 3.6.5
• PyTorch >= 1.2.0
• Please see environment.yml for more requirements

Install environment and download data

cd BioVNN
./install.sh

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Run experiments

0. Recommended resource

GPU Memory >=3GB is recommended

Currently it only supports GPU and CUDA

1. Activate environment

conda activate BioVNN

If it's activated, you will see (BioVNN) at the beginning of your command prompt

2. Example of running 5-fold cross-validation

cd src
./run_cv.sh

3. Example of running time-stamped experiment

• It is required to complete one cross-validation experiment before running the time-stamped experiment.
• Modify the parameter file params/timestamped.yml
• Make the load_result_dir_name=${the directory name of cross-validation result} For example: load_result_dir_name: 20201008201106_clh_v1_19Q3_rna_ep200_ES_p2_SS_ComF_l2_ce_Reactome_ref_PANC

cd src
./run_ts.sh

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Project organization

BioVNN/
├── README.md               <- This document.
├── install.sh              <- The script to set up environment and download data.
├── environment.yml         <- Conda environment file of package requirement.
└── src/                    <- Source code.
     ├── run_cv.py          <- The script to run 5-fold cross-validation of BioVNN.
     ├── run_cv_rg.py       <- The script to run 5-fold cross-validation of random group model.
     ├── run_cv_fc.py       <- The script to run 5-fold cross-validation of fully connected network.
     ├── run_ts.py          <- The script to run time-stamped experiments for BioVNN.
     ├── paths.py           <- The script to load environment variables.
     ├── biovnn_model.py    <- The class of BioVNN model.
     ├── dependency.py      <- The class of 5-fold cross-validation.
     ├── timestamped.py     <- The class of time-stamped experiment.
     ├── pytorch_layer.py   <- The class of PyTorch layers and dataloaders.
     ├── utils.py           <- The script of utility functions.
     └── set_logging.py     <- The script to set up log.