Identifying Drugs Associated With Parkinson's Disease Risk Using Machine Learning.

ABSTRACT: Machine learning (ML)-based methods have been proposed as a potential approach for identifying candidate drugs to be repurposed as disease-modifying treatments for Parkinson's disease (PD). We applied an ML-based signal detection method to identify drugs associated with PD and evaluated the method's generalizability. An algorithm combining subsampling and lasso logistic regression was implemented in a case-control study of 12 257 PD cases and 81 103 matched controls identified in Finnish registers. Drug exposure was defined at the subgroup and drug substance levels of the Anatomical Therapeutic Chemical (ATC) classification, considering the frequency of dispensation over 2 years, starting 10 years before the index date (8-year lag). Three subgroups and two individual drugs were associated

SUBMITTER: Pylkko E 

PROVIDER: S-EPMC12855170 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature