KCML: a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

摘要

Characterising context‐dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large‐scale genetic perturbation screens is based on analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge‐ and Context‐driven Machine Learning ( ), a framework that systematically predicts multiple context‐specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and β and signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight as a systematic framework for discovering novel scale‐crossing and context‐dependent gene functions. is highly generalisable and applicable to various large‐scale genetic perturbation screens.