Machine learning to dissect perturbations in complex cellular systems.

ABSTRACT: Understanding the responses of biological systems to various perturbations, such as genetic, chemical, or environmental challenges, is essential for reconstructing causal network models. Emerging single-cell technologies have become instrumental in elucidating cell states and phenotypes and they have been used in combination with genetic screening. Recent advances in machine learning and artificial intelligence architectures have stimulated the development of computational tools for modeling perturbations and the response to compounds. This study outlined core principles underpinning perturbation analysis and discussed the methodologies and analytical frameworks used to decode drug and genetic perturbation responses, complex multicellular interactions, and network dynamics. The current too

SUBMITTER: Monfort-Lanzas P 

PROVIDER: S-EPMC11915099 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature