Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC).

ABSTRACT: Background: Predicting the resistance profiles of antimicrobial resistance (AMR) pathogens is becoming more and more important in treating infectious diseases. Various attempts have been made to build machine learning models to classify resistant or susceptible pathogens based on either known antimicrobial resistance genes or the entire gene set. However, the phenotypic annotations are translated from minimum inhibitory concentration (MIC), which is the lowest concentration of antibiotic drugs in inhibiting certain pathogenic strains. Since the MIC breakpoints that classify a strain to be resistant or susceptible to specific antibiotic drug may be revised by governing institutes, we refrained from translating these MIC values into the categories "susceptible" or "resistant" but inst

SUBMITTER: Yang MR 

PROVIDER: S-EPMC10267731 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature