Repository Index

GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations

scPerturb: A resource and a python/R tool for single-cell perturbation data

• cite-seq
• database
• perturbation
• scatac-seq

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.

High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations

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