Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

ArXi:2603.07233v1 Announce Type: new Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning approaches have shown promise in modeling single-cell perturbation responses, they struggle to generalize across cell types and perturbation contexts due to limited contextual information during generation. We