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arXiv · 1801.08570

Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

Alexandr A. Kalinin·Gerald A. Higgins·Narathip Reamaroon·S. M. Reza Soroushmehr·Ari Allyn-Feuer·Ivo D. Dinov·Kayvan Najarian·Brian D. Athey
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Abstract

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions. Deep learning encapsulates a family of machine learning algorithms that over the last decade has transformed many important subfields of artificial intelligence (AI) 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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Alexandr A. Kalinin, Gerald A. Higgins, Narathip Reamaroon, S. M. Reza Soroushmehr, Ari Allyn-Feuer, Ivo D. Dinov, Kayvan Najarian, Brian D. Athey. 2018-01-25. Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification. https://doi.org/10.2217/pgs-2018-0008

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