Predicting genetic variants that affect gene regulation.
Official title: An ensemble framework for regulatory variant prediction.
Funded at Boston Children's Hospital · NIH-11044129 · Data updated 2026-11-12
This study is looking at how certain changes in our DNA that don't code for proteins can affect how our genes work, which is important for understanding different health conditions, and it's using smart computer techniques to find out which of these changes might be linked to diseases.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Boston Children's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | May 10, 2023 |
| Project end | February 29, 2028 |
| Funding year | 2025 |
| Award (this year) | $515,424 |
| NIH project number | 5R01HG012871-03 |
| Project ID | NIH-11044129 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with genetic disorders or those interested in understanding the genetic factors influencing their health.
✕Less likely to help
Patients with conditions not related to genetic regulatory variants may not benefit from this research.
What this research studies
This research focuses on understanding how specific genetic variants in noncoding regions of DNA can influence the expression of genes, which is crucial for various biological processes. By using advanced machine learning techniques, the team aims to identify these regulatory variants and their impact on human diseases. The approach involves training models on extensive genomic data to enhance the accuracy of predictions regarding which variants may disrupt gene regulation. This could lead to better insights into the genetic basis of diseases and traits.
Why it matters
Potential benefit: If successful, this research could lead to improved understanding and treatment of genetic disorders by identifying key regulatory variants.
How similar studies have gone: Previous research has shown success in using machine learning to predict regulatory variants, indicating a promising approach for this study.
Where this research is happening
-
Boston Children's Hospital
Boston, United States Active
Researchers
- Principal investigator: Lee, Dongwon — Boston Children's Hospital
- Study coordinator: Lee, Dongwon
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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.