Using advanced algorithms to understand genetics and health outcomes
Next-Generation Algorithms in Statistical Genetics Based on Modern Machine Learning
This study is using advanced computer technology to look at genetic and health information to help understand how our genes and environment affect our health, which could lead to better treatments and personalized care for patients like you.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cornell University NIH-funded |
| Lab location | 1 site (Ithaca, United States) |
| Project ID | NIH-10915565 on NIH RePORTER |
What this research studies
This research aims to harness the power of modern machine learning to analyze large datasets of human genomes and clinical information. By developing new algorithms, the project seeks to uncover how genetic and environmental factors influence health traits and disease outcomes. Patients may benefit from improved understanding of their genetic risks and more personalized medical therapies as a result of this work. The research will also produce open-source software tools that can be used by scientists and clinicians to apply these findings in real-world settings.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals with genetic conditions or those interested in understanding their genetic risk factors for various diseases.
Not a fit: Patients who do not have access to genetic testing or those with conditions unrelated to genetic factors may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to breakthroughs in personalized medicine and better health outcomes for patients.
How similar studies have performed: Other research has shown success in using machine learning to analyze genetic data, indicating that this approach has the potential for significant advancements.
Where this research is happening
Ithaca, United States
- Cornell University — Ithaca, United States (Active)
Researchers
- Principal investigator: Kuleshov, Volodymyr — Cornell University
- Study coordinator: Kuleshov, Volodymyr
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 below.