Genome Informatics and Machine Learning-Based Identification of Antimicrobial Resistance-Encoding Features and Virulence Attributes in Escherichia coli Genomes Representing Globally Prevalent Lineages, Including High-Risk Clonal Complexes

Genome Informatics and Machine Learning-Based Identification of Antimicrobial Resistance-Encoding Features and Virulence Attributes in Escherichia coli Genomes Representing Globally Prevalent Lineages, Including High-Risk Clonal Complexes

ABSTRACT Escherichia coli, a ubiquitous commensal/pathogenic member from the Enterobacteriaceae family, accounts for high infection burden, morbidity, and mortality throughout the world. With emerging multidrug resistance (MDR) on a massive scale, E. coli has been listed as one of the Global Antimic...

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Bibliographic Details
Main Authors: Sabiha Shaik, Anuradha Singh, Arya Suresh, Niyaz Ahmed
Format: Article
Language:English
Published: American Society for Microbiology 2022-02-01
Series:mBio
Subjects:
Escherichia coli
AMR surveillance
machine learning
virulence
bacterial evolution
genomics
Online Access:https://journals.asm.org/doi/10.1128/mbio.03796-21

Internet

https://journals.asm.org/doi/10.1128/mbio.03796-21

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