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 | Zendy

Sabiha Shaik | Zendy; Anuradha Singh | Zendy; Arya Suresh | Zendy; Niyaz Ahmed | Zendy

Open Access

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

Author(s) -

Sabiha Shaik,

Anuradha Singh,

Arya Suresh,

Niyaz Ahmed

Publication year - 2022

Publication title -

mbio

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.562

H-Index - 121

eISSN - 2161-2129

pISSN - 2150-7511

DOI - 10.1128/mbio.03796-21

Subject(s) - biology , genome , virulence , genetics , genomics , antibiotic resistance , multiple drug resistance , escherichia coli , comparative genomics , computational biology , gene , drug resistance , antibiotics

With the increase in the amounts of whole-genome data being generated, the application of relevant methods to mine biologically significant information from microbial genomes is of the utmost importance to public health genomics. Machine-learning methods have been used not just to predict or classify the data but also to identify the relevant features that could be linked with a particular class/target.

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