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- W4321493185 abstract "The declining cost of performing bacterial whole-genome sequencing (WGS) coupled with the availability of large libraries of sequence data for well-characterized isolates have enabled the application of machine-learning (ML) methods to the development of nonlinear sequence-based predictive models. We tested the ML-based model developed by Next Gen Diagnostics for prediction of cefepime phenotypic susceptibility results in Escherichia coli ." @default.
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- W4321493185 date "2023-03-23" @default.
- W4321493185 modified "2023-09-30" @default.
- W4321493185 title "Machine-Learning Model for Prediction of Cefepime Susceptibility in Escherichia coli from Whole-Genome Sequencing Data" @default.
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- W4321493185 doi "https://doi.org/10.1128/jcm.01431-22" @default.
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