Kernel multitask regression for toxicogenetics Journal Article


Authors: Bernard, E.; Jiao, Y.; Scornet, E.; Stoven, V.; Walter, T.; Vert, J. P.
Article Title: Kernel multitask regression for toxicogenetics
Abstract: 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. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim
Keywords: machine learning; kernel methods; multitask regression; toxicogenetics
Journal Title: Molecular Informatics
Volume: 36
Issue: 10
ISSN: 1868-1743
Publisher: Wiley V C H Verlag Gmbh  
Date Published: 2017-10-01
Start Page: 1700053
Language: English
DOI: 10.1002/minf.201700053
PROVIDER: scopus
PUBMED: 28949440
DOI/URL:
Notes: Article -- Export Date: 2 November 2017 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Elsa Bernard
    51 Bernard