Interpretable AI for Understanding How Cells Change in Cancers
Official title: Interpretable Machine Learning for Decoding Cell Dynamics
Funded at University of Illinois at Chicago · NIH-11429496 · Data updated 2026-11-12
A patient-facing explanation of a grant that uses smart computer models to understand how cancer cells change over time and in different environments to guide future treatments.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Illinois at Chicago NIH-funded |
| Lab location | 1 site (Chicago, UNITED STATES) |
| Project start | July 21, 2026 |
| Project end | April 30, 2031 |
| Funding year | 2026 |
| Award (this year) | $395,194 |
| NIH project number | 1R35GM166045-01 |
| Project ID | NIH-11429496 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients whose cancers are represented in the single-cell and imaging datasets used by the project, or whose tumor biology matches the study’s focus areas.
✕Less likely to help
Patients not represented in the datasets or with cancer types outside the study’s scope may see limited applicability of the findings.
What this research studies
This project develops easy-to-interpret machine learning tools that analyze large biological datasets from cancer patients, aiming to separate the different factors that drive cell behavior. The researchers will combine data from single-cell experiments, tissue imaging, and knowledge from scientific literature to create models whose outputs can be understood in terms of specific cellular processes. The goal is to uncover clear, biologically meaningful patterns in how cancer cells react to their surroundings and to perturbations. The work focuses on human cancer biology and strives to produce insights that could inform future therapies. If successful, these interpretable models could help doctors tailor treatments based on how cells in a patient’s tumor are likely to respond to changes in the microenvironment.
Why it matters
Potential benefit: If successful, the models could lead to better understanding of cancer cell dynamics and help guide more personalized treatment strategies.
How similar studies have gone: Prior work using interpretable AI in biology has shown promise in disentangling complex cellular factors, with several approaches demonstrating actionable insights.
Where this research is happening
-
University of Illinois at Chicago
Chicago, United States Active
Researchers
- Principal investigator: Chen, Hao — University of Illinois at Chicago
- Study coordinator: Chen, Hao
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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.