气候变化领域集成服务门户(CCPortal): Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model

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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).