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DOI | 10.1073/pnas.2018877118 |
Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model | |
Im H.; Hwang S.-H.; Kim B.S.; Choi S.H. | |
发表日期 | 2021 |
ISSN | 0027-8424 |
卷号 | 118期号:20 |
英文摘要 | 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 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. © 2021 National Academy of Sciences. All rights reserved. |
英文关键词 | Machine learning; Pathogenic potential; Risk assessment; STEC |
语种 | 英语 |
scopus关键词 | article; bacterium isolate; controlled study; nonhuman; pathogenicity; risk assessment; risk factor; Shiga toxin producing Escherichia coli; support vector machine; whole genome sequencing |
来源期刊 | Proceedings of the National Academy of Sciences of the United States of America |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/238920 |
作者单位 | National Research Laboratory of Molecular Microbiology and Toxicology, Seoul National University, Seoul, 08826, South Korea; Department of Agricultural Biotechnology, Center for Food Safety and Toxicology, Seoul National University, Seoul, 08826, South Korea; Department of Food Science and Engineering, Ewha Womans University, Seoul, 03760, South Korea; Center for Food and Bioconvergence, Seoul National University, Seoul, 08826, South Korea |
推荐引用方式 GB/T 7714 | Im H.,Hwang S.-H.,Kim B.S.,et al. Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model[J],2021,118(20). |
APA | Im H.,Hwang S.-H.,Kim B.S.,&Choi S.H..(2021).Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model.Proceedings of the National Academy of Sciences of the United States of America,118(20). |
MLA | Im H.,et al."Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model".Proceedings of the National Academy of Sciences of the United States of America 118.20(2021). |
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