Advanced AI tools to connect big biomedical data for better genes and personalized care
Official title: Advanced machine learning models to integrate multi-modal biomedical datasets for gene regulation and precision medicine
Funded at University of North Texas · NIH-11362387 · Data updated 2026-11-12
A researcher team plans to build open-source computer methods that combine genomics, EHRs, and other health data to better predict patient outcomes and find new drug uses.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of North Texas NIH-funded |
| Lab location | 1 site (Denton, United States) |
| Project start | August 1, 2019 |
| Project end | June 30, 2030 |
| Funding year | 2026 |
| Award (this year) | $395,979 |
| NIH project number | 5R35GM133657-08 |
| Project ID | NIH-11362387 on NIH RePORTER |
Who could benefit from this research
✓Could help
Researchers and healthcare professionals who work with large, diverse health datasets or multi-omics data.
✕Less likely to help
Direct patient benefits may be limited for individuals not involved in or impacted by data-driven research.
What this research studies
This project develops general, easy-to-use AI tools that can handle diverse health data from millions of people. The work focuses on making multi-modal data (like genetics, epigenetics, transcriptomics, and electronic health records) work together through interpretable machine learning and graph-based approaches. The goal is to improve prediction of clinical outcomes, map regulatory networks, and identify existing drugs that could help treat other conditions. It emphasizes openness, reproducibility, and robustness to data gaps and noise. Researchers aim to create tools that clinicians and researchers can apply to real-world health questions across diseases.
Why it matters
Potential benefit: If successful, patients could benefit from more accurate risk predictions and faster identification of effective treatments or repurposed drugs.
How similar studies have gone: Prior multi-omics and EHR integration studies have shown promise, with growing use of interpretable ML in healthcare.
Where this research is happening
-
University of North Texas
Denton, United States Active
Researchers
- Principal investigator: Bozdag, Serdar — University of North Texas
- Study coordinator: Bozdag, Serdar
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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.