Deep learning in pharmacogenomics: from gene regulation to patient stratification release_tkhmrqkevjfqxdty6ttbw33jam

by Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey

Published in Pharmacogenomics (London) by Future Medicine Ltd.

2018   Volume 19, Issue 7, p629-650

Abstract

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.
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Type  article-journal
Stage   published
Date   2018-04-26
Language   en ?
DOI  10.2217/pgs-2018-0008
PubMed  29697304
PMC  PMC6022084
Wikidata  Q52309127
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ISSN-L:  1462-2416
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