Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

ABSTRACT: The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation

SUBMITTER: Moradigaravand D 

PROVIDER: S-EPMC6310291 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature