Pathogenic potential assessment of the Shiga toxin–producing <i>Escherichia coli</i> by a source attribution–considered machine learning model
Hanhyeok Im, Seung-Ho Hwang, Byoung Sik Kim, Sang Ho Choi
Abstract
(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, the SVM model, discriminated between the two control groups most accurately. The SVM model successfully predicted the pathogenicity of the isolates from the major sources of STEC outbreaks, the isolates with the history of outbreaks, and the isolates that cannot be assessed by conventional methods. Furthermore, the SVM model effectively differentiated the pathogenic potentials of the isolates at a finer resolution. Permutation importance analyses of the input dataset further revealed the genes important for the estimation, proposing the genes potentially essential for the pathogenicity of STEC. Altogether, these results suggest that the SVM model is a more reliable and broadly applicable method to evaluate the pathogenic potential of STEC isolates compared with conventional methods.