Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

TL;DR

This paper reviews how deep learning advances are transforming pharmacogenomics, enabling better gene regulation understanding, patient stratification, and drug interaction predictions, with future potential for personalized medicine.

Contribution

It highlights current applications and future prospects of deep learning in pharmacogenomics, emphasizing its role in personalized medicine and complex data analysis.

Findings

Deep learning identifies regulatory variants in noncoding regions.

It enables patient stratification from medical records.

Predicts drugs, targets, and interactions effectively.

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.