Improving the detection of genetic variations in human genomes
Official title: Deep learning methods for genotyping structural variants in human genomes
Funded at Middlebury College · NIH-10796022 · Data updated 2026-11-12
This study is working on new ways to find important changes in our DNA that can help us understand diseases better, using smart computer techniques to make the process more accurate and helpful for doctors and researchers.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Middlebury College NIH-funded |
| Lab location | 1 site (Middlebury, United States) |
| Project start | September 16, 2024 |
| Project end | August 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $375,839 |
| NIH project number | 1R15HG012859-01A1 |
| Project ID | NIH-10796022 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with known genetic disorders or those at risk for genetic diseases due to family history.
✕Less likely to help
Patients with no genetic predisposition to diseases or those without identifiable structural variants may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of identifying structural variants (SVs) in human genomes, which are crucial for understanding various diseases. By utilizing deep learning techniques, the project aims to develop advanced tools that can better analyze short read genome sequencing data. The approach involves treating SV genotyping as an image similarity problem, allowing for more precise identification of diverse SVs. This could lead to improved molecular diagnosis and a deeper understanding of genetic diversity and disease mechanisms.
Why it matters
Potential benefit: If successful, this research could significantly enhance the accuracy of genetic testing, leading to better diagnosis and treatment options for patients with genetic diseases.
How similar studies have gone: Previous research has shown promise in using machine learning for genetic analysis, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
-
Middlebury College
Middlebury, United States Active
Researchers
- Principal investigator: Linderman, Michael David — Middlebury College
- Study coordinator: Linderman, Michael David
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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.