Developing algorithms to analyze gene expression data in Alzheimer's disease
Official title: Geometric structures guided learning model and algorithms for bulk RNAseq data analysis
Funded at University of North Carolina Charlotte · NIH-10894919 · Data updated 2026-11-12
This study is looking at how to better understand gene activity in brain tissue from people with Alzheimer's disease, using smart computer techniques to pinpoint important genes that could help us find new treatments just for you.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of North Carolina Charlotte NIH-funded |
| Lab location | 1 site (Charlotte, United States) |
| Project start | September 28, 2022 |
| Project end | July 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $181,999 |
| NIH project number | 5R01GM148971-03 |
| Project ID | NIH-10894919 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for participation or benefit from this research include individuals diagnosed with Alzheimer's disease or those at risk of developing it.
✕Less likely to help
Patients with other forms of dementia unrelated to Alzheimer's may not receive direct benefits from this research.
What this research studies
This research focuses on improving the analysis of gene expression data from bulk tissue samples, particularly in the context of Alzheimer's disease. By using advanced computational algorithms, the study aims to accurately identify differentially expressed genes that are crucial for understanding disease mechanisms. The approach involves deconvoluting complex gene expression data to isolate signals from specific cell types, which can lead to better insights into potential treatments. Patients may benefit from the findings as they could inform the development of targeted therapies for Alzheimer's disease.
Why it matters
Potential benefit: If successful, this research could lead to more effective treatments for Alzheimer's disease by identifying key genes involved in its progression.
How similar studies have gone: Previous research has shown promise in using computational algorithms for gene expression analysis, suggesting that this approach could yield valuable insights.
Where this research is happening
-
University of North Carolina Charlotte
Charlotte, United States Active
Researchers
- Principal investigator: Chen, Duan — University of North Carolina Charlotte
- Study coordinator: Chen, Duan
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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.