Using advanced machine learning to understand genetic factors in Alzheimer's disease
Causal and integrative deep learning for Alzheimer's disease genetics
This study is exploring new ways to use computer technology to look at genetic information related to Alzheimer's disease, with the goal of finding out what increases or decreases the risk of developing it, so that we can improve how we diagnose and treat the condition for patients like you.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Minnesota NIH-funded |
| Lab location | 1 site (Minneapolis, United States) |
| Project ID | NIH-10898859 on NIH RePORTER |
What this research studies
This research focuses on developing innovative machine learning techniques to analyze genetic and phenotypic data related to Alzheimer's disease. By leveraging large-scale genomic studies and advanced computational methods, the project aims to identify causal risk and protective factors for Alzheimer's. The approach includes integrating various types of biological data to enhance the understanding of the genetic basis of the disease, which could lead to improved diagnostic and therapeutic strategies. Patients may benefit from insights gained through this research that could inform future treatments and prevention strategies.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals with a family history of Alzheimer's disease or those showing early signs of cognitive decline.
Not a fit: Patients with non-genetic forms of dementia or those without any familial link to Alzheimer's may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to better understanding and potentially new treatments for Alzheimer's disease.
How similar studies have performed: Previous research utilizing machine learning and genomic data has shown promise in identifying genetic factors in various diseases, indicating a potential for success in this novel approach.
Where this research is happening
Minneapolis, United States
- University of Minnesota — Minneapolis, United States (Active)
Researchers
- Principal investigator: Pan, Wei — University of Minnesota
- Study coordinator: Pan, Wei
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.