Open AccessGenome 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,
A. 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.