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Significance Outbreaks of newly emerging pathogens have posed serious threats to public health. Here, we developed a machine learning (ML) model to evaluate the pathogenic potential of the Shiga toxin–producingEscherichia coli (STEC) isolates using their whole-genome sequencing data. The developed ML model using the support vector machine (SVM) algorithm, the SVM model, correctly estimated the pathogenic potentials of the previous outbreak isolates. The SVM model was also more reliable and widely applicable to predict the pathogenicity of STEC than the conventional assessment methods. Permutation importance analyses further identified the genes potentially associated with the pathogenicity of STEC. This ML-based approach will be a promising assessment method to identify the risk of newly emerging pathogens.

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