Kernel Multitask Regression for Toxicogenetics

Elsa Bernard, Yunlong Jiao, Erwan Scornet, Veronique Stoven, Thomas Walter, Jean Philippe Vert

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4 Citations (Scopus)

Résumé

The development of high-throughput in vitro assays to study quantitatively the toxicity of chemical compounds on genetically characterized human-derived cell lines paves the way to predictive toxicogenetics, where one would be able to predict the toxicity of any particular compound on any particular individual. In this paper we present a machine learning-based approach for that purpose, kernel multitask regression (KMR), which combines chemical characterizations of molecular compounds with genetic and transcriptomic characterizations of cell lines to predict the toxicity of a given compound on a given cell line. We demonstrate the relevance of the method on the recent DREAM8 Toxicogenetics challenge, where it ranked among the best state-of-the-art models, and discuss the importance of choosing good descriptors for cell lines and chemicals.

langue originaleAnglais
Numéro d'article1700053
journalMolecular Informatics
Volume36
Numéro de publication10
Les DOIs
étatPublié - 1 oct. 2017
Modification externeOui

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