Understanding how bacteria become resistant to antibiotics using advanced computer techniques
Official title: Leveraging evolutionary analyses and machine learning to discover multiscale molecular features associated with antibiotic resistance
Funded at University of Colorado Denver · NIH-10906831 · Data updated 2026-11-12
This study is looking into how certain dangerous bacteria become resistant to antibiotics, which can make infections harder to treat, and it aims to help doctors find better ways to fight these infections in the future.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Colorado Denver NIH-funded |
| Lab location | 1 site (Aurora, UNITED STATES) |
| Project start | August 14, 2023 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $451,200 |
| NIH project number | 5U01AI176414-02 |
| Project ID | NIH-10906831 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who are infected with or at risk of infections caused by antibiotic-resistant bacteria.
✕Less likely to help
Patients with infections caused by non-ESKAPE pathogens may not receive direct benefits from this research.
What this research studies
This research investigates the mechanisms behind antibiotic resistance (AR) in harmful bacteria known as ESKAPE pathogens, which are responsible for many severe infections. By combining evolutionary analyses with machine learning, the project aims to integrate various data types to uncover how these pathogens adapt and develop resistance over time. The findings will be made accessible through open data repositories and software platforms, allowing researchers and clinicians to better predict and combat AR in emerging bacterial strains. Patients may benefit from improved treatment strategies as a result of this research.
Why it matters
Potential benefit: If successful, this research could lead to more effective strategies for preventing and treating antibiotic-resistant infections.
How similar studies have gone: Previous research has shown promise in using machine learning and evolutionary analyses to understand antibiotic resistance, indicating that this approach could yield valuable insights.
Where this research is happening
-
University of Colorado Denver
Aurora, United States Active
Researchers
- Principal investigator: Ravi, Janani — University of Colorado Denver
- Study coordinator: Ravi, Janani
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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.