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

Molecular Systems Biology
Heba SailemLucas Pelkmans

Abstract

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 ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), 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 KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT 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 KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable t...Continue Reading

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Citations

Aug 9, 2020·Frontiers in Bioengineering and Biotechnology·Meletios-Nikolaos DoulgkeroglouDimitrios I Zeugolis
Oct 3, 2020·Computational and Structural Biotechnology Journal·Avelino JaverHeba Z Sailem
Jun 18, 2021·Cell Systems·Leah V Schaffer, Trey Ideker

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Methods Mentioned

BETA
ubiquitination
classifier prediction

Software Mentioned

GPS
HT
kstest2 MatLab
R
enrichment
Cytoscape
CellProfiler
Pathway
STRING
MATLAB

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