Predicting gene essentiality and drug response from preclinical perturbation screens with layered ensemble of autoencoders and predictors.

Barbara Bodinier¹, Gaetan Dissez², Lucile Ter-Minassian², Linus Bleistein², Roberta Codato², John Klein², Eric Durand², Antonin Dauvin²

¹Owkin, Inc, New York, NY, USA. barbara.bodinier@owkin.com.

²Owkin, Inc, New York, NY, USA.

PubMed DOI

Abstract

High-throughput preclinical perturbation screens, where the effects of genetic, chemical, or environmental perturbations are systematically tested on disease models, hold significant promise for machine learning-enhanced drug discovery due to their scale and causal nature. Predictive models trained on such datasets can be used to (i) infer perturbation response for previously untested disease mode…

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