Treatment options for people infected with antibiotic resistant bacteria today are limited and may become even more so over time. The risk of a post-antibiotic era, in which even minor infections or routine medical procedures could be fatal, is real. Beyond high personal costs, the economic burden of drug resistance is estimated to cost more than $1 billion each year in North America alone.
Genomic screening of pathogens to determine their identity and responsiveness to antibiotics could be both more accurate and efficient than other ways of diagnosing infectious diseases and choosing the appropriate treatment. The challenge lies in analyzing the large amount of data produced by this sequencing to understand the genomic factors underlying drug resistance and create an accurate model to predict drug resistance based on sequencing data.
In this project, Drs. Leonid Chindelevitch, Maxwell Libbrecht and Jesse Shapiro developed machine learning tools to better understand the complex relationships between bacterial genome sequences and antibiotic resistance. The team also produced user friendly tools for researchers and clinicians to predict the antimicrobial resistance of a specific bacterial sample. These gears have provided insights into drug resistance and advanced computational design and approaches.