Journal
PHARMACOGENOMICS
Volume 19, Issue 7, Pages 629-650Publisher
FUTURE MEDICINE LTD
DOI: 10.2217/pgs-2018-0008
Keywords
adverse events; artificial intelligence; deep learning; drug discovery; drug-drug interaction; drug-gene interaction; noncoding regulatory variation; patient stratification; pharmacogenomics
Categories
Funding
- NIH [P20 NR015331, U54 EB020406, P50 NS091856, P30 DK089503, P30 AG053760, T32 GM070449]
- National Science Foundation [1734853, 1636840, 1416953, 0716055, 1023115]
- Elsie Andresen Fiske Research Fund
- University of Michigan Medical School
- Direct For Education and Human Resources [0716055] Funding Source: National Science Foundation
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [1734853] Funding Source: National Science Foundation
- Division Of Undergraduate Education [0716055] Funding Source: National Science Foundation
- Division Of Undergraduate Education
- Direct For Education and Human Resources [1023115, 1416953] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1636840] Funding Source: National Science Foundation
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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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