Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens.

ABSTRACT: The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to systematically elucidate the genetic basis for AMR. Here, we present a generalizable machine learning workflow for identifying genetic features driving AMR based on constructing reference strain-agnostic pan-genomes and training random subspace ensembles (RSEs). This workflow was applied to the resistance profiles of 14 antimicrobials across three urgent threat pathogens encompassing 288 Staphylococcus aureus, 456 Pseudomonas aeruginosa, and 1588 Escherichia coli genomes. We find that feature selection by RSE detects known AMR associations more relia

SUBMITTER: Hyun JC 

PROVIDER: S-EPMC7067475 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature