A clever machine-learning method to understand genetic factors in cleft lip and palate
Official title: A functional smoothed machine learning approach for high-dimensional orofacial clefts genetic data analysis
Funded at University of New Hampshire · NIH-11374630 · Data updated 2026-11-12
A high‑dimensional genetic data project uses new machine‑learning ideas to better understand and predict risk for orofacial clefts in newborns.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of New Hampshire NIH-funded |
| Lab location | 1 site (Durham, United States) |
| Project start | August 19, 2026 |
| Project end | August 18, 2028 |
| Funding year | 2026 |
| Award (this year) | $299,950 |
| NIH project number | 1R03DE034770-01A1 |
| Project ID | NIH-11374630 on NIH RePORTER |
Who could benefit from this research
✓Could help
Newborns and families affected by orofacial clefts, or those at risk due to genetic factors, may benefit from advances in risk prediction and gene discovery.
✕Less likely to help
Patients without orofacial cleft risk factors or familial relevance may not directly benefit, though broader methods could still inform future studies.
What this research studies
This project combines deep neural networks with functional data analysis to handle many genetic variables and missing data, aiming to predict cleft risk and highlight potential disease genes. It will train and test the model on large orofacial cleft databases, focusing on how genetic patterns relate to birth defects. The work emphasizes making advanced analytics suitable for real-world genetic data and complex relationships among variants.
Why it matters
Potential benefit: If successful, it could improve early risk prediction and help identify genes involved in cleft conditions, guiding future research and potential interventions.
How similar studies have gone: Previous approaches show promise when combining deep learning with genetic data, but this specific FDA/OC combination is novel.
Where this research is happening
-
University of New Hampshire
Durham, United States Active
Researchers
- Principal investigator: Geng, Pei — University of New Hampshire
- Study coordinator: Geng, Pei
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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.