AI to find biological markers linked to exceptional longevity
An Explainable Unified AI Strategy for Efficient and Robust Integrative Analysis of Multi-omics Data from Highly Heterogeneous Multiple Studies
This project uses explainable artificial intelligence to combine many types of genetic and molecular data to find markers tied to living to very old ages, focusing on centenarians and related cohorts.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Jackson Laboratory NIH-funded |
| Lab location | 1 site (Bar Harbor, United States) |
| Project ID | NIH-11376322 on NIH RePORTER |
What this research studies
Researchers will develop explainable AI tools that link markers within and across different types of biological data (multi-omics) and across many studies. The methods use graph neural networks to learn shared patterns while keeping results interpretable. The team will apply the tools to human longevity cohorts such as the Long-Life Family Study and the Integrative Longevity Omics data to find pathways and biomarkers associated with exceptional longevity. They will also compare human findings with omics data from about 100 other species to spot conserved and species-specific longevity signals.
Who could benefit from this research
Good fit: People included in longevity or aging cohorts—especially centenarians, their relatives, or participants in the Long-Life Family Study or EL consortium—are the ideal contributors to this research.
Not a fit: Patients with acute non-aging-related conditions or those not part of participating cohorts are unlikely to receive direct benefits from this analysis.
Why it matters
Potential benefit: If successful, this work could reveal biomarkers and biological pathways that help predict healthy aging and guide new prevention or treatment strategies for age-related conditions.
How similar studies have performed: Previous studies have used AI and multi-omics to study aging, but combining many heterogeneous human studies and cross-species data with an explainable graph neural network is a novel approach.
Where this research is happening
Bar Harbor, United States
- Jackson Laboratory — Bar Harbor, United States (Active)
Researchers
- Principal investigator: Li, Yi — Jackson Laboratory
- Study coordinator: Li, Yi
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.