Understanding how genes affect diabetes through advanced data analysis
Official title: GeneSight: Advancing Insights into Complex Genetic Traits through CFDE-Enhanced Knowledge Graphs
Funded at Univ of North Carolina Chapel Hill · NIH-10990226 · Data updated 2026-11-12
This study is working on a smart tool that uses computer technology to help us understand the genetic reasons behind adult-onset diabetes, so that patients can get better treatment options based on this information.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | September 5, 2024 |
| Project end | September 4, 2025 |
| Funding year | 2024 |
| Award (this year) | $311,000 |
| NIH project number | 1R03OD038393-01 |
| Project ID | NIH-10990226 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals diagnosed with adult-onset diabetes who are interested in understanding the genetic factors influencing their condition.
✕Less likely to help
Patients without a diagnosis of adult-onset diabetes or those not interested in genetic research may not receive benefits from this research.
What this research studies
This research aims to develop a machine learning tool that utilizes visible neural networks to better understand the genetic factors contributing to adult-onset diabetes. By integrating diverse biological data from the Common Fund Data Ecosystem, the project will create a knowledge graph that organizes and connects genetic information with disease outcomes. Patients can benefit from this research as it seeks to translate complex genetic data into actionable health insights, potentially leading to improved treatment strategies for diabetes. The project will also provide a query-able resource for researchers and clinicians to access this integrated data.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective treatment options for patients with adult-onset diabetes.
How similar studies have gone: Previous research utilizing machine learning and knowledge graphs has shown promise in enhancing our understanding of genetic traits and their implications for health.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Chung, Cheng-Han — Univ of North Carolina Chapel Hill
- Study coordinator: Chung, Cheng-Han
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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.