Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model.

ABSTRACT: Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)-based method using whole-genome sequencing (WGS) data are getting attention because of the recent advances in ML algorithms and sequencing technologies. Here, we developed various ML models to predict the pathogenicity of Shiga toxin-producing Escherichia coli (STEC) isolates using their WGS data. The input dataset for the ML models was generated using distinct gene repertoires from positive (pathogenic) and negative (nonpathogenic) control groups in which each STEC isolate was designated based on the source attribution, the relative risk potential of the isolation sources. Among the va

SUBMITTER: Im H 

PROVIDER: S-EPMC8157976 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature