Using deep learning to predict disease risk from genetic information
Official title: Predicting individual disease risk for individuals harboring monogenic risk alleles with deep learning
Funded at Icahn School of Medicine at Mount Sinai · NIH-11045188 · Data updated 2026-11-12
This study is looking at how your genes might affect your risk of getting certain diseases, especially for those with rare genetic traits, and it aims to create a better way to predict your risk by combining genetic information with other factors, which could help doctors make more personalized treatment decisions for you.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | April 8, 2025 |
| Project end | February 28, 2030 |
| Funding year | 2025 |
| Award (this year) | $579,403 |
| NIH project number | 1R01HG013511-01A1 |
| Project ID | NIH-11045188 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who carry rare monogenic risk alleles and are interested in understanding their potential disease risks.
✕Less likely to help
Patients without any known genetic risk alleles or those with common diseases may not benefit from this research.
What this research studies
This research aims to enhance our understanding of how genetic factors influence disease risk, particularly for individuals with rare monogenic risk alleles. By employing advanced deep learning techniques, the project seeks to integrate both genetic and non-genetic factors to create a more accurate prediction model for disease risk. This approach will help clarify the relationship between genetic risk and actual disease development, potentially leading to improved genetic diagnostics. The ultimate goal is to provide a comprehensive risk assessment that can inform clinical decisions and precision medicine.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of disease risk for individuals with specific genetic profiles, improving personalized healthcare.
How similar studies have gone: Previous research using deep learning for genetic risk prediction has shown promise, indicating that this approach could yield significant advancements in understanding disease risk.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Jordan, Daniel Michael — Icahn School of Medicine at Mount Sinai
- Study coordinator: Jordan, Daniel Michael
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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.