Fig. 1: A diagram illustrating the present analysis.

a Collecting longitudinal clinical data from the Parkinson’s Progression Markers Initiative (PPMI) and Parkinson’s Disease Biomarkers Program (PDBP) cohorts and conducting necessary data cleaning and preprocessing. b Development of a deep phenotypic progression embedding (DPPE) model to learn a progression embedding vector for each individual, which encodes his/her PD symptom progression trajectory. c Cluster analysis with the learned embedding vectors to identify PD subtypes, each of which reveal a unique PD progression pattern. d Identifying CSF biomarkers and imaging markers the discovered PD subtypes. e Construction of PD subtype-specific molecular modules based on genetic and transcriptomic data, along with human protein-protein interactome (PPI) network analyses, using network medicine approaches. f In silico drug repurposing based on subtype-specific molecular profiles and validation of drug candidates’ treatment efficiency based on analysis of large-scale real-world patient databases, i.e., the INSIGHT and OneFlorida + . g Architecture of the DPPE model. Specifically, DPPE engaged two Long-Short Term Memory (LSTM) units—one as encoder receiving an individual’s longitudinal clinical records and compacting them into a low-dimensional embedding space; while another taking the individual’s embedding vector to reconstruct the original clinical records. DPPE was trained by minimizing the reconstruction difference.