Decoding Valley Fever with AI to understand genomic and clinical clues
Official title: Decoding Coccidioides: An LLM AI Approach to Genomic, Phenotypic, and Clinical Characterization of 200 Clinical Isolates
Funded at Mayo Clinic Arizona · NIH-11355014 · Data updated 2026-11-12
A Mayo Clinic team will use an AI approach to study 200 patient-derived Coccidioides isolates to find genomic and clinical patterns that predict how valley fever progresses.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Mayo Clinic Arizona NIH-funded |
| Lab location | 1 site (Scottsdale, United States) |
| Project start | September 1, 2026 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $220,796 |
| NIH project number | 1R21AI199162-01 |
| Project ID | NIH-11355014 on NIH RePORTER |
Who could benefit from this research
✓Could help
People diagnosed with valley fever (coccidioidomycosis) whose clinical data and fungal isolates are available for study.
✕Less likely to help
Patients without access to linked clinical data or fungal isolates may not directly benefit from this research.
What this research studies
This project uses an AI language model and data tools to analyze genomic data and detailed patient records from people with valley fever. Researchers will link genomic features of the fungus to clinical outcomes such as pneumonia, meningitis, or chronic disease, and examine how antibody titers mirror disease changes during treatment. By building a longitudinal, annotated dataset that combines symptoms, lab results, and imaging, they aim to identify markers that better predict prognosis and response to antifungal therapy. The work seeks to generate insights that could guide future diagnostics and vaccine-related research, benefiting patients with valley fever.
Why it matters
Potential benefit: If successful, patients could receive earlier and more accurate prognostic information and more targeted treatment choices.
How similar studies have gone: Previous work has shown that integrating genomic and clinical data can reveal prognostic patterns, but this project applies advanced AI to a larger, longitudinal dataset for valley fever.
Where this research is happening
-
Mayo Clinic Arizona
Scottsdale, United States Active
Researchers
- Principal investigator: Grys, Thomas E — Mayo Clinic Arizona
- Study coordinator: Grys, Thomas E
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.