Biomedical & Clinical Informatics Lab

Department of Computational Medicine & Bioinformatics

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

TitleDeep learning in pharmacogenomics: from gene regulation to patient stratification.
Publication TypeJournal Article
Year of Publication2018
AuthorsKalinin AA, Higgins GA, Reamaroon N, Soroushmehr SMR, Allyn-Feuer A, Dinov ID, Najarian K, Athey BD
JournalPharmacogenomics
Volume19
Issue7
Pagination629-650
Date Published2018 May
ISSN1744-8042
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.

DOI10.2217/pgs-2018-0008
Alternate JournalPharmacogenomics
PubMed ID29697304
PubMed Central IDPMC6022084
Grant ListP20 NR015331 / NR / NINR NIH HHS / United States