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

ABSTRACT: Identifying genetic drivers of chronic diseases is necessary for drug discovery. Here, we develop a machine learning-assisted genetic priority score, which we call ML-GPS, that incorporates genetic associations with predicted disease phenotypes to enhance target discovery. First, we construct gradient boosting models to predict 112 chronic disease phecodes in the UK Biobank and analyze associations of predicted and observed phenotypes with common, rare, and ultra-rare variants to model the allelic series. We integrate these associations with existing evidence using gradient boosting with continuous feature encoding to construct ML-GPS, training it to predict drug indications in Open Targets and externally testing it in SIDER. We then generate ML-GPS predictions for 2,362,636 gene-phecode p

SUBMITTER: Chen R 

PROVIDER: S-EPMC11480483 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature