Multimodal physical evidence uncovers interpretable gene regulatory networks for perturbation prediction (opens in new tab)

Gene regulatory networks govern cell fate transitions through dynamic causal mechanisms. Since exhaustively mapping this vast perturbation space experimentally is prohibitive, scalable computational models are essential. Yet, current frameworks fall short because they infer statistical co-expression rather than physical mechanisms, remain blind to non-canonical regulators lacking classical DNA-binding motifs, and fail to generalize across unseen perturbation factors or cell lines. Here we sho...