Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
Year: 2018
Type: review
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.
Source: Pharmacogenomics
Authors: Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
Institutions: University of Michigan, Michigan Medicine
Language: English
Subfield: Pharmacology
Domain: Life Sciences
Open Access status: green
Funders: National Institutes of Health, National Institute on Aging, National Institute of Nursing Research, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
Awards: Michigan Alzheimer's Disease Core Center, Big Data for Discovery Science, The University of Michigan Bioinformatics Training Program, Cholinergic mechanisms of attentional-motor integration and gait dysfunction in Parkinson Disease, Center for Complexity and Self-management of Chronic Disease (CSCD), Michigan Nutrition Obesity Research Center
OpenAlex ID: W2784341656