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Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

Medicine and Health

Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

R. Chen, A. Duffy, et al.

Discover ML-GPS, a groundbreaking machine learning-assisted genetic priority score developed by Robert Chen and colleagues, designed to revolutionize drug target discovery for chronic diseases. This innovative approach not only enhances prediction accuracy but also uncovers thousands of potential gene-phenotype pairs, paving the way for new drug targets in clinical trials.

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Playback language: English
Abstract
This study introduces ML-GPS, a machine learning-assisted genetic priority score, to enhance drug target discovery for 112 chronic diseases. ML-GPS integrates genetic associations with predicted disease phenotypes, improving upon the original GPS framework. Using gradient boosting models and continuous feature encoding, ML-GPS significantly improves prediction accuracy and expands drug target coverage, identifying thousands of previously unsupported gene-phenotype pairs and promising targets for drugs in clinical trials.
Publisher
Nature Communications
Published On
Oct 15, 2024
Authors
Robert Chen, Aine Duffy, Ben O. Petrizzini, Ha My Vy, David Stein, Matthew Mort, Joshua K. Park, Avner Schlessinger, Yuval Itan, David N. Cooper, Daniel M. Jordan, Ghislain Rocheleau, Ron Do
Tags
machine learning
genetic associations
drug target discovery
chronic diseases
gradient boosting models
prediction accuracy
gene-phenotype pairs

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