Analyzing cell populations using advanced imaging and machine learning techniques
Official title: Cryo-Electron Tomography derived cell population analysis through novel high-throughput machine learning approaches
Funded at Carnegie-Mellon University · NIH-11090133 · Data updated 2026-11-12
This study is exploring new ways to take detailed 3D pictures of cells to help doctors better understand diseases and create better treatments, which could lead to improved diagnoses and drug designs for patients.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| 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 | February 28, 2030 |
| Funding year | 2025 |
| Award (this year) | $371,427 |
| NIH project number | 1R35GM158094-01 |
| Project ID | NIH-11090133 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions that involve cellular abnormalities or diseases that could benefit from enhanced diagnostic techniques.
✕Less likely to help
Patients with stable conditions that do not involve cellular analysis or those not requiring advanced imaging techniques may not benefit from this research.
What this research studies
This research focuses on using cryo-electron tomography (Cryo-ET) to create detailed 3D images of cells and their internal structures. By developing new machine learning methods, the project aims to analyze differences in cell populations and their subcellular components more efficiently. Patients may benefit from improved disease diagnosis and drug design based on a better understanding of cellular structures. The research will tackle challenges related to analyzing diverse data from various cell types to enhance the accuracy and applicability of findings.
Why it matters
Potential benefit: If successful, this research could lead to more accurate disease diagnoses and innovative drug designs tailored to specific cellular characteristics.
How similar studies have gone: Previous research has shown promise in using machine learning for image analysis in biology, indicating potential success for this novel approach.
Where this research is happening
-
Carnegie-Mellon University
Pittsburgh, United States Active
Researchers
- Principal investigator: Xu, Min — Carnegie-Mellon University
- Study coordinator: Xu, Min
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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.