Understanding how language models and topology predict RNA:DNA R-loop formation in breast cancer biology
Official title: Integrating Language Models and Topology for Multi-Scale R-Loop Prediction
Funded at University of California at Davis · NIH-11536586 · Data updated 2026-11-12
Researchers will build models that combine AI language models with topology ideas to predict how R-loops form and behave in breast cancer-relevant genetic sequences.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California at Davis NIH-funded |
| Lab location | 1 site (Davis, United States) |
| Project start | September 2, 2026 |
| Project end | August 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $409,731 |
| NIH project number | 1R01GM169397-01 |
| Project ID | NIH-11536586 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates include individuals whose breast cancer biology involves genome instability or researchers seeking translational implications of R-loop biology.
✕Less likely to help
Patients without breast cancer or without interest in genome instability mechanisms are unlikely to directly benefit.
What this research studies
This project aims to create a multi-scale predictive framework that combines experimental data with new mathematical and machine learning models to understand R-loop formation. By integrating single-molecule measurements, DNA topology experiments, and Atomic Force Microscopy, it seeks to reveal how topology and sequence context drive R-loop structure. The team will use probabilistic grammars, tile-based representations, and large language models to forecast R-loop formation on real genomic sequences. The work is designed to connect physical DNA properties with computational predictions to illuminate genome stability in cancer.
Why it matters
Potential benefit: If successful, the work could offer new insights into breast cancer biology and identify potential targets related to genome instability driven by R-loops.
How similar studies have gone: Previous studies show promise in combining experimental data with computational models to predict nucleic acid structures, but this multi-scale, topology-focused approach is novel.
Where this research is happening
-
University of California at Davis
Davis, United States Active
Researchers
- Principal investigator: Arsuaga, Francisco Javier — University of California at Davis
- Study coordinator: Arsuaga, Francisco Javier
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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.