Developing tools to analyze complex genomic data from tissues
Official title: Machine learning methods for interpreting spatial multi-omics data
Funded at Columbia Univ New York Morningside · NIH-11019779 · Data updated 2026-11-12
This study is working on new computer tools to help scientists understand how our genes and the environment work together in our tissues, which could improve our knowledge of how diseases develop and how different cells interact with each other.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Columbia Univ New York Morningside NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | February 17, 2023 |
| Project end | January 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $375,276 |
| NIH project number | 5R01HG012875-03 |
| Project ID | NIH-11019779 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions that involve complex tissue interactions, such as cancer or neurological disorders.
✕Less likely to help
Patients with conditions that do not involve spatially-resolved genomic analysis may not benefit from this research.
What this research studies
This research focuses on creating advanced computational tools to analyze and integrate data from new genomic technologies that provide spatial information about tissues. By using machine learning techniques, the project aims to uncover how environmental interactions influence normal development and disease. The researchers will develop models that can interpret complex data from various sources, such as gene expression and protein levels, to identify unique patterns in tissue composition. This innovative approach could lead to a better understanding of how different cell types interact within their environments.
Why it matters
Potential benefit: If successful, this research could lead to improved diagnostic and therapeutic strategies for diseases by providing deeper insights into tissue biology.
How similar studies have gone: Other research has shown promise in using machine learning for genomic data analysis, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
-
Columbia Univ New York Morningside
New York, United States Active
Researchers
- Principal investigator: Azizi, Elham — Columbia Univ New York Morningside
- Study coordinator: Azizi, Elham
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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.