Implementation of Bipartite Graph-represented Drug Response Predictor (BiG-DRP and BiG-DRP+) as described in:

David Earl Hostallero, Yihui Li, Amin Emad, Looking at the BiG picture: incorporating bipartite graphs in drug response prediction, Bioinformatics, Volume 38, Issue 14, 15 July 2022, Pages 3609–3620, https://doi.org/10.1093/bioinformatics/btac383

This repository has been tested on python 3.7. To install the dependencies run the following on the terminal

pip install -r requirements.txt

python main.py

To run BiG-DRP+, you must first run BiG-DRP while specifying the results subfolder (--folder=<folder_name>). Then run BiG-DRP with the --weight_folder specified as the results subfolder in the previous run.

python main.py --mode=train --folder=big
python main.py --mode=extra --weight_folder=big --folder=big_plus

• --split: the type of data-splitting to use (lco or lpo, default: lco)
• --dataroot: the root directory of your data (file names for input files can me modified in utils/constants.py) (default: ../)
• --outroot: the root directory of your outputs (default: ./)
• --folder: subdirectory you want to save your outputs (optional)
• --weight_folder: subdirectory for the saved weights and encodings (for BiG-DRP+ only)
• --mode: train means BiG-DRP, extra means BiG-DRP+ (default: train)
• --seed: the seed number for 5-fold CV (default: 0)
• --drug_feat: type of drug feature (desc, morgan, or mixed, default: desc)
• --network_perc: percentile used for the bipartite graph threshold (default: 1)

Preprocessed data can be accessed here: https://dx.doi.org/10.6084/m9.figshare.20022947

@article{hostallero2022looking,
  title={Looking at the BiG picture: incorporating bipartite graphs in drug response prediction},
  author={Hostallero, David Earl and Li, Yihui and Emad, Amin},
  journal={Bioinformatics},
  volume={38},
  number={14},
  pages={3609--3620},
  year={2022},
  publisher={Oxford University Press}
}