Using machine learning to analyze complex biological data from tissues
Official title: Integrative Machine Learning for Common Fund Spatial Omics
Funded at Carnegie-Mellon University · NIH-11127006 · Data updated 2026-11-12
This study is working on new computer techniques to better understand how cells behave and interact in tissues, which could help us learn more about diseases and develop more effective treatments for patients.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Carnegie-Mellon University NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | June 10, 2025 |
| Project end | May 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $286,327 |
| NIH project number | 1R03OD039980-01 |
| Project ID | NIH-11127006 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions that involve complex cellular interactions, such as cancer or other diseases affecting tissue organization.
✕Less likely to help
Patients with conditions that do not involve significant cellular or tissue-level interactions may not benefit from this research.
What this research studies
This research focuses on developing advanced machine learning techniques to analyze spatial transcriptomics data, which provides insights into how cells interact and function within tissues. By integrating diverse datasets from various NIH programs, the project aims to create a comprehensive framework that can reveal the mechanisms of gene expression in different cellular environments. Patients can benefit from this research as it enhances our understanding of diseases at a cellular level, potentially leading to more targeted therapies. The approach involves creating scalable models that can process and analyze large amounts of biological data effectively.
Why it matters
Potential benefit: If successful, this research could lead to improved understanding of cellular interactions and disease mechanisms, ultimately informing better treatment strategies for patients.
How similar studies have gone: Other research has shown promise in using machine learning for analyzing biological data, indicating that this approach could be effective.
Where this research is happening
-
Carnegie-Mellon University
Pittsburgh, United States Active
Researchers
- Principal investigator: Ma, Jian — Carnegie-Mellon University
- Study coordinator: Ma, Jian
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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.