Unlocking antimicrobial resistance with multiomics and machine learning
The global antimicrobial resistance (AMR) emergency is driven by complex and evolving molecular mechanisms. Cutting-edge machine learning methods and multiomics technologies can help to combat this crisis by predicting novel AMR biomarkers and outcomes with unprecedented precision and speed, offering critical insights into the molecular underpinnings of AMR.
Authors
- Charmie K. Vang (ORCID: https://orcid.org/0000-0001-5724-0265)
- Janani Ravi (ORCID: https://orcid.org/0000-0001-7443-925X)
- Abhirupa Ghosh (ORCID: https://orcid.org/0000-0002-3980-4749)
- Evan P. Brenner (ORCID: https://orcid.org/0009-0000-7067-8886)
Institutions
- University of Colorado Anschutz Medical Campus (US)
Publication Details
- Journal
- Trends in Microbiology
- Published
- 2025-05-25
- DOI
- https://doi.org/10.1016/j.tim.2025.04.017
- Citations
- 8
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 12.03
Funders
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases