Developing machine learning tools to analyze single-cell genomic and proteomic data
Official title: Semi-supervised cross-modality translation for single-cell genomics and proteomics
Funded at University of Washington · NIH-10983900 · Data updated 2026-11-12
This study is working on smart computer programs to help us learn more about how individual cells behave, especially in conditions like cancer, so that we can find better, personalized treatments for patients.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | September 4, 2024 |
| Project end | June 30, 2025 |
| Funding year | 2024 |
| Award (this year) | $129,723 |
| NIH project number | 1K99HG013343-01A1 |
| Project ID | NIH-10983900 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for participation or benefit from this research would include individuals with cancers or other conditions that involve complex cellular behaviors.
✕Less likely to help
Patients with stable, well-characterized conditions that do not involve significant cellular variability may not receive direct benefits from this research.
What this research studies
This research focuses on creating advanced machine learning algorithms to better understand single-cell genomic and proteomic data. By using semi-supervised learning techniques, the project aims to predict various cellular profiles from existing measurements, which could help fill in gaps in our knowledge about cellular functions and behaviors. Patients may benefit from insights gained through this research, particularly in understanding how different cells behave in various conditions, including cancer. The research will involve computational modeling to infer changes in cellular profiles over time, which could lead to more personalized treatment approaches.
Why it matters
Potential benefit: If successful, this research could lead to improved understanding and treatment of diseases at the cellular level, particularly in cancer.
How similar studies have gone: Other research has shown promise in using machine learning for genomic and proteomic analysis, indicating that this approach could yield valuable insights.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Zhang, Ran — University of Washington
- Study coordinator: Zhang, Ran
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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.