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• W4212915964 abstract "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." @default.
• W4212915964 created "2022-02-24" @default.
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• W4212915964 date "2022-02-22" @default.
• W4212915964 modified "2023-10-18" @default.
• W4212915964 title "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" @default.
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• W4212915964 doi "https://doi.org/10.1128/mbio.03796-21" @default.
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