Research Area:  Machine Learning

Abstract:

Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.

Keywords:  
Rare-Variant Pharmacogenomics
Deep Learning
Machine Learning

Author(s) Name:  Yitian Zhou, Roman Tremmel, Elke Schaeffeler, Matthias Schwab, Volker M. Lauschke

Publisher name:  Elsevier

DOI:  10.1016/j.tips.2022.07.002

Volume Information:  Volume 43, Issue 10, October 2022, Pages 852-865

Paper Link:   https://www.sciencedirect.com/science/article/pii/S0165614722001717