Using machine learning to understand the genetics of Alzheimer's disease
Official title: Interpretable machine learning methods for the analysis of Alzheimers disease genetics
Funded at Stanford University · NIH-11135758 · Data updated 2026-11-12
This study is looking at how certain genes might play a role in Alzheimer's disease in people aged 65 and older, using smart computer techniques to help us understand these genetic links better, which could lead to improved treatments in the future.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | May 15, 2025 |
| Project end | March 31, 2030 |
| Funding year | 2025 |
| Award (this year) | $762,636 |
| NIH project number | 1R01AG089509-01A1 |
| Project ID | NIH-11135758 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals aged 65 and older who are at risk for or diagnosed with Alzheimer's disease.
✕Less likely to help
Patients under the age of 65 or those without a genetic predisposition to Alzheimer's disease may not benefit from this research.
What this research studies
This research focuses on applying advanced machine learning techniques to analyze genetic data related to Alzheimer's disease (AD), particularly in individuals aged 65 and older. By leveraging recent advancements in genome sequencing, the study aims to identify causal genetic variants that contribute to AD, which are currently poorly understood. The approach includes using explainable artificial intelligence methods to enhance the interpretability of the machine learning models, allowing researchers to quantify how specific genetic changes may influence the development of the disease. This could lead to more effective drug trials and better understanding of AD genetics.
Why it matters
Potential benefit: If successful, this research could lead to improved identification of genetic risk factors for Alzheimer's disease, potentially resulting in better-targeted treatments.
How similar studies have gone: Other research has shown promise in using machine learning approaches for genetic analysis, indicating that this method could yield significant insights into Alzheimer's disease.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: He, Zihuai — Stanford University
- Study coordinator: He, Zihuai
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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.