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https://github.com/bowang-lab/congfu

CongFu: Conditional Graph Fusion for Drug Synergy Prediction

https://github.com/bowang-lab/congfu

Science Score: 36.0%

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CongFu: Conditional Graph Fusion for Drug Synergy Prediction

Basic Info
  • Host: GitHub
  • Owner: bowang-lab
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 5.02 MB
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Created about 3 years ago · Last pushed almost 3 years ago
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Readme

README.md

CongFu

This is the official codebase for CongFu: Conditional Graph Fusion for Drug Synergy Prediction.

What is CongFu?

Drug synergy, characterized by the amplified combined effect of multiple drugs, presents a critical phenomenon for optimizing therapeutic outcomes. However, limited data on drug synergy, arising from the vast number of possible drug combinations and computational costs, motivate the need for predictive methods. In this work, we introduce CongFu, a novel Conditional Graph Fusion Layer, designed to predict drug synergy. CongFu employs an attention mechanism and a bottleneck to extract local graph contexts and conditionally fuse graph data within a global context. Its modular architecture enables flexible replacement of layer modules, including readouts and graph encoders, facilitating customization for diverse applications. To evaluate the performance of CongFu, we conduct comprehensive experiments on four datasets, encompassing three distinct setups for drug synergy prediction. Remarkably, CongFu achieves state-of-the-art results on 11 out of 12 benchmark datasets, demonstrating its ability to capture intricate patterns of drug synergy. Through extensive ablation studies, we validate the significance of individual layer components, affirming their contributions to overall predictive performance. By addressing the challenge of predicting drug synergy in untested drug pairs, CongFu opens new avenues for optimizing drug combinations and advancing personalized medicine. More details can be found in the paper.

Installation

The following command will install the required packages for CongFu with virtual environment:

bash python create -n congfu python=3.9 conda activate congfu cd CongFu pip install -e .

Loading Preprocessed Data

All datasets used in the paper can be downloaded via the commands: bash wget -P ./data/preprocessed https://www.dropbox.com/s/91z87vf857o12pz/drugcomb.zip unzip ./data/preprocessed/drugcomb.zip -d ./data/preprocessed rm ./data/preprocessed/drugcomb.zip

Preprocessing

To replicate preprocessing: * Download Gene Expression data and Drug Combination data via the commands: bash wget -P ./data/init https://www.cancerrxgene.org/gdsc1000//GDSC1000_WebResources//Data/preprocessed/Cell_line_RMA_proc_basalExp.txt.zip unzip ./data/init/Cell_line_RMA_proc_basalExp.txt.zip -d ./data/init rm ./data/init/Cell_line_RMA_proc_basalExp.txt.zip wget -P ./data/init https://drugcomb.fimm.fi/jing/summary_v_1_5.csv * For each synergy score (loewe, zip, hsa, bliss) run following commands: bash python preprocessing/process_drugcomb.py --synergy_score your_score python preprocessing/create_transductive_splits.py --synergy_score your_score python preprocessing/create_transductive_splits.py --synergy_score your_score

Training

The following command will train CongFu-based model on the first fold on leave-comb inductive setup: bash python train.py --num_layers 5 --inject_layer 3 --emb_dim 300 --feature_dim 512 --context_dim 908 --synergy_score loewe --inductive_set_name leave_comb --fold_number 0 --batch_size 128 --lr 1e-4 --number_of_epochs 100

Citation

bibtex @misc{tsepa2023congfu, title={CongFu: Conditional Graph Fusion for Drug Synergy Prediction}, author={Oleksii Tsepa and Bohdan Naida and Bo Wang}, year={2023}, eprint={2305.14517}, archivePrefix={arXiv}, primaryClass={cs.LG} }

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