logo
ResearchBunny Logo
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.

00:00
00:00
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review
Medicine and Health

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

J. Zhang, F. Zhong, et al.

Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system
Engineering and Technology

Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system

Z. Che, X. Wan, et al.

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis
Computer Science

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis

A. Izzidien

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort
Psychology

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

D. H. M. Pelt, P. C. Habets, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny