Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
It addresses the problem of improving personalized medicine and drug efficacy for patients, but it is incremental as it reviews existing and future applications without presenting new results.
This perspective explores the application of deep learning in pharmacogenomics to tackle problems like identifying regulatory variants, patient stratification, and predicting drug interactions, with the result being anticipated widespread use for personalized drug response prediction and medication optimization.
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.