Project Grant R01NS140142

This Project Grant award, totaling $684,451, was provided by the National Institute of Neurological Disorders and Stroke (NINDS) under the Extramural Research Programs in the Neurosciences and Neurological Disorders (CFDA 93.853) federal grant program. The primary objective is to develop an integrative machine learning pipeline to identify Parkinson's disease (PD) progression subtypes and potential drug targets through analysis of longitudinal clinical, transcriptomic, and neuroimaging data. Key activities include: 1) Identifying PD progression subtypes by integrating clinical, transcriptomic, and neuroimaging data from the Parkinson's Progression Markers Initiative and Parkinson Disease Biomarkers Program cohorts; 2) Discovering gene modules governing differential PD progression through integrative analysis of multi-omics data; and 3) Evaluating and validating gene modules as drug targets via in-silico drug repurposing using multi-omics, a biomedical knowledge graph, and real-world patient data. The project will involve sub-awards to researchers at the Cleveland Clinic Lerner College of Medicine and Temple University to contribute computational methods, clinical expertise, and data interpretation.