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

Abstract excerpt

(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 various ML models examined, a model using the support vector machine (SVM) algorithm,...

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