Machine learning tools for subcellular spatial biology
Official title: Machine learning and statistical tools for subcellular spatial biology
Funded at Georgia Institute of Technology · NIH-11429808 · Data updated 2026-11-12
A grant to build and test advanced computer methods that analyze subcellular patterns in tissue samples to better understand diseases at the cellular level.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Georgia Institute of Technology NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | August 1, 2026 |
| Project end | May 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $326,942 |
| NIH project number | 1R01LM015378-01 |
| Project ID | NIH-11429808 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal for researchers and clinicians who work with tissue samples and spatial transcriptomics data to understand diseases.
✕Less likely to help
Direct patient benefits may be limited in the short term while the toolkit is being developed and validated.
What this research studies
This project will create a toolkit that uses machine learning and statistics to analyze subcellular spatial transcriptomics data, going beyond whole-cell analyses. It aims to identify where genes and gene modules are located inside cells, and how these patterns change across different conditions. The work will emphasize interpretable results that could reveal new biomarker patterns linked to tissue function and pathology. Researchers will test the toolkit on SST data to uncover mechanistic insights and potential diagnostic opportunities.
Why it matters
Potential benefit: If successful, patients could benefit from more precise biomarkers and insights into disease processes at the cellular level.
How similar studies have gone: Earlier SST studies show promise, but this project focuses on subcellular resolution and novel biomarker patterns, making it relatively novel with potential high impact.
Where this research is happening
-
Georgia Institute of Technology
Atlanta, United States Active
Researchers
- Principal investigator: Sinha, Saurabh — Georgia Institute of Technology
- Study coordinator: Sinha, Saurabh
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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.