Using advanced algorithms to understand genetics and health outcomes
Official title: Next-Generation Algorithms in Statistical Genetics Based on Modern Machine Learning
Funded at Cornell University · NIH-10915565 · Data updated 2026-11-12
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 start | September 1, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $407,754 |
| NIH project number | 5R35GM151243-02 |
| Project ID | NIH-10915565 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with genetic conditions or those interested in understanding their genetic risk factors for various diseases.
✕Less likely to help
Patients who do not have access to genetic testing or those with conditions unrelated to genetic factors may not benefit from this research.
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.
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 gone: 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
-
Cornell University
Ithaca, United States Active
Researchers
- Principal investigator: Kuleshov, Volodymyr — Cornell University
- Study coordinator: Kuleshov, Volodymyr
Related research
Other NIH-funded projects on the same topics.
-
PORTENT 3.0 Technical Core for global health point-of-care tools
A NIH U54 grant supports Cornell and partners in advancing point-of-care technologies for nutrition, infection, and cancer to real-world use, especially in global health settings.
-
Point-of-care zinc test strips to verify infant zinc medicines in low-resource settings
A fast, low-cost smartphone‑based test strips method checks zinc-containing infant medicines in places with limited lab access.
-
Center for Spillover Prevention and One Health Readiness in Asia
A center in Asia works to prevent pandemics by studying how animal infections jump to humans and by building tools to detect and respond quickly to spillovers.
-
SAGE: A privacy-friendly system to monitor antimicrobial use in animals
A nationwide, privacy-preserving data hub will securely collect and standardize antimicrobial use data across animal species to support smarter stewardship and regulatory science.
-
Rapid, bedside KS diagnosis in Africa with KS-COMPLETE
A fast, point-of-care test using skin biopsies to diagnose Kaposi’s sarcoma in sub-Saharan Africa, helping patients get results in about an hour.
-
Nuclear mechanics in tight cell movement
Scientists are studying how the nucleus changes its shape and function when cells squeeze through very small spaces, which could affect health and disease.
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.