Combination of whole genome sequencing and supervised machine learning provides unambiguous identification of eae-positive Shiga toxin-producing Escherichia coli

INTRODUCTION: The objective of this study was to develop, using a genome wide machine learning approach, an unambiguous model to predict the presence of highly pathogenic STEC in E. coli reads assemblies derived from complex samples containing potentially multiple E. coli strains. Our approach has t...

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Detalles Bibliográficos
Autores principales: Vorimore, Fabien, Jaudou, Sandra, Tran, Mai-Lan, Richard, Hugues, Fach, Patrick, Delannoy, Sabine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Microbiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213463/
https://www.ncbi.nlm.nih.gov/pubmed/37250024
http://dx.doi.org/10.3389/fmicb.2023.1118158

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213463/
https://www.ncbi.nlm.nih.gov/pubmed/37250024
http://dx.doi.org/10.3389/fmicb.2023.1118158

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