Improving understanding of gene regulatory networks using advanced machine learning techniques.
Official title: Toward Deep Learning Techniques for Cell-Type and Spatial Resolution Estimation of Regulatory Networks
Funded at University of Illinois at Urbana-Champaign · NIH-10988806 · Data updated 2026-11-12
This study is exploring how to better understand how genes work together in individual cells by using advanced computer techniques to look at detailed data from those cells, which could help us learn more about how they function and interact in their surroundings.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Illinois at Urbana-Champaign NIH-funded |
| Lab location | 1 site (Champaign, United States) |
| Project start | September 5, 2024 |
| Project end | September 4, 2025 |
| Funding year | 2024 |
| Award (this year) | $290,326 |
| NIH project number | 1R03OD038389-01 |
| Project ID | NIH-10988806 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions related to gene expression and cellular dysfunction, particularly those affecting brain and nervous system health.
✕Less likely to help
Patients with conditions unrelated to gene regulatory networks or those not involving cellular functions may not benefit from this research.
What this research studies
This research focuses on enhancing our understanding of gene regulatory networks by utilizing advanced deep learning techniques to analyze spatial transcriptomics data. By moving beyond traditional methods that rely on bulk tissue samples, the study aims to capture the complexities of individual cells and their environments. The researchers will develop machine learning models that can effectively estimate regulatory networks at a single-cell level, addressing challenges related to data scarcity and spatial relationships. This innovative approach promises to provide deeper insights into gene expression and cellular functions.
Why it matters
Potential benefit: If successful, this research could lead to significant advancements in personalized medicine by improving our understanding of how genes regulate cellular functions in specific contexts.
How similar studies have gone: While the application of deep learning in spatial transcriptomics is a relatively novel approach, preliminary studies have shown promise in similar methodologies.
Where this research is happening
-
University of Illinois at Urbana-Champaign
Champaign, United States Active
Researchers
- Principal investigator: Wang, Haohan — University of Illinois at Urbana-Champaign
- Study coordinator: Wang, Haohan
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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.