Repository Index

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

• disentanglement
• drug-discovery
• genomics
• perturbation

PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.

PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning

DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.

GNN-based gene-selection deep learning model for drug response prediction