Using AI to find Parkinson's progression types and potential drug targets
Progression Subtyping and Drug Target Identification for Parkinson's Disease with Integrative Machine Learning
This project uses artificial intelligence to identify groups of people with Parkinson's who follow different progression paths and to point to molecules that could become new drug targets.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Weill Medical Coll of Cornell Univ NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-11238950 on NIH RePORTER |
What this research studies
Researchers will combine large Parkinson's datasets—clinical records, biological (multi-omics) data, and brain imaging—from resources like PPMI and PDBP with real-world patient data and a biomedical knowledge graph. They will develop machine learning methods that integrate these diverse data types to define distinct progression subtypes of Parkinson's disease. The team will also use those integrated models to nominate biological targets that might be suitable for drug development. Findings will be used to guide future studies and possibly help match patients to more tailored therapies.
Who could benefit from this research
Good fit: People diagnosed with Parkinson's disease—especially older adults (65+)—who can share medical records, imaging, or biological samples would be most relevant to this effort.
Not a fit: People without Parkinson's disease or those seeking an immediate change to their clinical care are unlikely to benefit directly from this computational research.
Why it matters
Potential benefit: If successful, this work could help identify patient groups who might respond differently to treatments and reveal new targets for therapies that slow or stop Parkinson's progression.
How similar studies have performed: Previous studies have used machine learning on single data types to find markers in Parkinson's, but fully integrating multi-omics, imaging, real-world data, and knowledge graphs for subtype and target discovery is newer and less proven.
Where this research is happening
New York, United States
- Weill Medical Coll of Cornell Univ — New York, United States (Active)
Researchers
- Principal investigator: Su, Chang — Weill Medical Coll of Cornell Univ
- Study coordinator: Su, Chang
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.