Learning Genetic Perturbation Effects with Variational Causal Inference.

ABSTRACT: Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict respons

SUBMITTER: Liu E 

PROVIDER: S-EPMC12157634 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature