Progression Subtyping and Drug Target Identification for Parkinson's Disease with Integrative Machine Learning
Project Number1R01NS140142-01
Contact PI/Project LeaderSU, CHANG
Awardee OrganizationWEILL MEDICAL COLL OF CORNELL UNIV
Description
Abstract Text
ABSTRACT
Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder all over the world. It is
estimated that PD affects 2-3% of people older than 65 years. The underlying etiology and pathophysiology of
PD remain unclear to date. Furthermore, PD patients show great heterogeneity in disease progression
throughout the PD course, which is a critical factor that hinders therapeutic development. This creates challenges
in finding effective disease-modifying treatment or prevention strategies. To overcome the challenges, massive
resources for PD study have been built up and become available for research, including clinical, multi-omics,
and neuroimaging data generated from well-designed research initiatives such as the Parkinson's Progression
Markers Initiative (PPMI) and the Parkinson Disease Biomarkers Program (PDBP); general data sources in
biology such as protein-protein interactome network data and functional genomic data; a comprehensive
biomedical knowledge graph (BKG) we built; and continuously increasing volume of real-world patient data
(RWD). Integrative analysis of these massive and heterogeneous data poses considerable challenges to
conventional computational approaches for deriving valuable and reliable insights. Despite the numerous efforts
to develop novel machine learning (ML) algorithms for analyzing these data, they typically focused on one or a
few data types. Therefore, there is a critical need to develop ML methods to perform integrative and effective
analyses of heterogeneous PD data sources to derive comprehensive insights. This project aims to build such a
pipeline with three specific aims. Aim 1 identifies progression subtypes of PD through integrative modeling of
longitudinal clinical, transcriptomic, and neuroimaging data of participants in the PPMI and PDBP cohorts. Aim
2 identifies gene modules that govern the differential PD progression through integrative analysis of multi-omics
data with network medicine and ML. Aim 3 evaluates and validates the gene modules as drug targets through
in-silico drug repurposing with multi-omics, BKG we built, and real-world patient data, respectively. In sum, this
pipeline will perform integrative analysis on the longitudinal clinical data, transcriptomics data, and neuroimaging
data, as well as whole-genome/exome sequencing data from the PPMI and PDBP cohorts, publicly available
human interactome data, functional genomic data, drug-perturbation multi-omics data, our BKG, and the real-
world EHR data from the INSIGHT network (covering ~12 million patients across New York City's Five health
systems and the greater metropolitan area), the Cleveland CIinic EHR database (covering ~11 million patients
extracted from IBM Explorys), and the Temple Health EHR (covering ~1.2 million patients).
Public Health Relevance Statement
PROJECT NARRATIVE
The goal of this project is to build an integrative machine learning pipeline that 1) derives progression subtypes
of Parkinson's Disease (PD) from longitudinal clinical assessments, RNA-seq data, and neuroimage acquisitions
of individuals diagnosed with PD; 2) identifies subtype-specific gene modules that govern differential PD
progression (i.e., progression subtypes) with a diverse set of individual-level genetics and multi-omics data
related to PD, functional genomic data, and human protein-protein interactome; 3) evaluates and validates the
identified gene modules as drug targets through in-silico drug repurposing using multi-omics, biomedical
knowledge graph, and real world patient-level electronic health records (EHR). The developed algorithms and
software tools will be made publicly available and widely disseminated in the PD research communities.
National Institute of Neurological Disorders and Stroke
CFDA Code
853
DUNS Number
060217502
UEI
YNT8TCJH8FQ8
Project Start Date
01-December-2024
Project End Date
30-November-2029
Budget Start Date
01-December-2024
Budget End Date
30-November-2025
Project Funding Information for 2025
Total Funding
$684,451
Direct Costs
$512,345
Indirect Costs
$172,106
Year
Funding IC
FY Total Cost by IC
2025
National Institute of Neurological Disorders and Stroke
$684,451
Year
Funding IC
FY Total Cost by IC
Sub Projects
No Sub Projects information available for 1R01NS140142-01
Publications
Publications are associated with projects, but cannot be identified with any particular year of the project or fiscal year of funding. This is due to the continuous and cumulative nature of knowledge generation across the life of a project and the sometimes long and variable publishing timeline. Similarly, for multi-component projects, publications are associated with the parent core project and not with individual sub-projects.
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Outcomes
The Project Outcomes shown here are displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed are those of the PI and do not necessarily reflect the views of the National Institutes of Health. NIH has not endorsed the content below.
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Clinical Studies
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History
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