Interpretable deep learning to map genetics and epigenetics of Alzheimer's at single-cell level
Official title: Interpretable Deep Learning Methods to Investigate Genetics and Epigenetics of Alzheimer's Disease at a Single-Cell Resolution
Funded at University of California-Irvine · NIH-11383572 · Data updated 2026-11-12
A new project will use advanced computer models to understand how genetic and epigenetic changes drive Alzheimer's disease at the level of individual brain cells.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California-Irvine NIH-funded |
| Lab location | 1 site (Irvine, United States) |
| Project start | August 30, 2022 |
| Project end | July 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $616,382 |
| NIH project number | 5R01NS128523-05 |
| Project ID | NIH-11383572 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults aged 21 and older with or at risk for Alzheimer's disease or related dementias may benefit from insights and tools developed.
✕Less likely to help
Individuals without ADRD or related risk may not directly gain from this computational study.
What this research studies
This grant funds development of interpretable deep learning methods and software to reveal how genetic, epigenetic, network, and chromatin changes contribute to ADRD in single cells. The approach will integrate large, diverse single-cell omics data to create a personalized brain regulome that reflects cell-type specificity. Researchers will tackle challenges like missing data and high dimensionality to produce clinically meaningful insights. The goal is to provide tools that help identify mechanisms and potential targets for intervention in Alzheimer's and related dementias.
Why it matters
Potential benefit: If successful, the work could lead to new biomarkers and therapeutic targets tailored to cellular changes in ADRD.
How similar studies have gone: Prior studies show promise for single-cell analyses in ADRD, but this project aims for interpretable models specifically tying multi-omic data to cell-type–level dysregulations.
Where this research is happening
-
University of California-Irvine
Irvine, United States Active
Researchers
- Principal investigator: Zhang, Jing — University of California-Irvine
- Study coordinator: Zhang, Jing
Related research
Other NIH-funded projects on the same topics.
-
AIM-AI: an actionable, integrated, multiscale genetic map of Alzheimer’s disease powered by deep learning
A research project uses advanced AI to connect genetic data, biology, and brain scans to better understand Alzheimer’s disease and its risk factors.
-
Deep Learning Retina Tool to Find Early Alzheimer’s
A new AI-based retinal imaging tool aims to help identify people at risk for Alzheimer’s before symptoms appear using a quick, noninvasive eye scan.
-
Deep learning to uncover neurovascular disruptions in Alzheimer's disease
A neuron-and-blood-vessel focused study uses advanced computer methods to find molecular changes in brain blood vessels of people with Alzheimer’s disease.
-
Global shared-AI network to improve Alzheimer's detection and prediction
This project builds a secure, shared AI system that learns from brain scans and medical data from people worldwide to help spot and predict Alzheimer's for patients and those at risk.
-
AI-built genetic map to understand Alzheimer's disease
Using artificial intelligence to combine genetic, molecular, and brain imaging data to find genetic clues that could help people with or at risk for Alzheimer's disease.
-
Understanding Alzheimer's Disease through Brain Scans and Genetics
This project uses advanced computer analysis of brain scans and genetic information to better understand Alzheimer's disease in older adults.
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.