Using machine learning to improve disease risk prediction for diverse populations.
Official title: Development and application of machine learning-based approaches for estimating disease risk in diverse and admixed populations.
Funded at Galatea Bio INC · NIH-10921442 · Data updated 2026-11-12
This study is working on improving genetic risk assessments for diseases so that they are more accurate for people from different backgrounds, making it easier for everyone to benefit from personalized medicine.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Galatea Bio INC NIH-funded |
| Lab location | 1 site (Hialeah, United States) |
| Project start | September 19, 2024 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $399,156 |
| NIH project number | 1R43HG013628-01 |
| Project ID | NIH-10921442 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals from diverse and admixed ancestry backgrounds who are interested in understanding their genetic risk for diseases.
✕Less likely to help
Patients with a homogeneous European ancestry may not receive significant benefits from this research as existing models are already optimized for their demographic.
What this research studies
This research focuses on developing advanced machine learning techniques to create more accurate genetic risk assessments for diseases, particularly for individuals from diverse and mixed ancestry backgrounds. It aims to enhance existing polygenic risk scores (PRS) by evaluating their effectiveness across different populations and generating a more inclusive ancestry reference panel. The project will also implement a user-friendly application of these models, making them accessible for clinical use. By addressing the limitations of current PRS models, this research seeks to ensure that personalized medicine benefits a broader range of individuals.
Why it matters
Potential benefit: If successful, this research could lead to more accurate disease risk predictions, allowing for better prevention and management strategies tailored to diverse populations.
How similar studies have gone: Previous research has shown promise in using machine learning for genetic risk prediction, but this approach aims to fill a gap by focusing specifically on diverse populations, making it a novel endeavor.
Where this research is happening
-
Galatea Bio INC
Hialeah, United States Active
Researchers
- Principal investigator: Wall, Jeffrey D — Galatea Bio INC
- Study coordinator: Wall, Jeffrey D
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.