Commoditization of near-real-time large-scale genomic sequencing of microorganisms, and the insights provided by evolutionary dynamics and machine learning, provides new opportunities for treating antibiotic resistance and for understanding how it develops.
Areas of Focus
- Antibiotic Resistance Evolution
- Host–Pathogen Interactions
Faculty
News
Got Resistance? (April 29, 2019)
When it comes to TB, the computer has the answer
Predicting TB's Behavior (August 3, 2017)
Can new molecular tests beat standard lab cultures in predicting TB outcome?
Resistance Fighters (July 17, 2017)
Maha Farhat and Michael Baym come together in DBMI to stem the rising tide of drug resistance
Publications
Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction (EBioMedicine, 2019)
Fluoroquinolone Resistance Mutation Detection Is Equivalent to Culture-Based Drug Sensitivity Testing for Predicting Multidrug-Resistant Tuberculosis Treatment Outcome: A Retrospective Cohort Study (Clinical Infectious Diseases, 2017)