Using advanced neural networks to understand genetic changes in diverse populations
Official title: Adaptive evolutionary inference frameworks for understudied populations using generative neural networks
Funded at Haverford College · NIH-10875841 · Data updated 2026-11-12
This study is looking at new ways to understand genetic information from different groups of people using advanced computer techniques, so we can better learn about genetic changes and how they relate to our history as humans.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Haverford College NIH-funded |
| Lab location | 1 site (Haverford, United States) |
| Project start | December 18, 2020 |
| Project end | May 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $350,141 |
| NIH project number | 2R15HG011528-02 |
| Project ID | NIH-10875841 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals from understudied or diverse genetic backgrounds who may benefit from insights into their genetic heritage.
✕Less likely to help
Patients with well-studied genetic backgrounds or those not interested in genetic research may not receive direct benefits from this research.
What this research studies
This research focuses on improving how we analyze genetic data from various human populations using advanced deep learning techniques. By developing new frameworks that utilize generative neural networks, the project aims to enhance our understanding of genetic mutations and demographic histories. The researchers will compare these new methods with traditional approaches to determine their effectiveness in interpreting complex genetic information. Ultimately, the goal is to provide clearer insights into how evolutionary processes affect different populations.
Why it matters
Potential benefit: If successful, this research could lead to better understanding and management of genetic diversity and its implications for health and disease in various populations.
How similar studies have gone: Other research has shown promise in using deep learning for genetic analysis, indicating that this approach could yield significant advancements in the field.
Where this research is happening
-
Haverford College
Haverford, United States Active
Researchers
- Principal investigator: Mathieson, Sara — Haverford College
- Study coordinator: Mathieson, Sara
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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.