Machine-Learning Model for Prediction of Cefepime Susceptibility in Escherichia coli from Whole-Genome Sequencing Data
Year: 2023
Type: article
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 .
Source: Journal of Clinical Microbiology
DOI: 10.1128/jcm.01431-22
Authors: Romney M. Humphries, Eugene Bragin, Julian Parkhill, Grace Morales, Jonathan E. Schmitz, Paul A. Rhodes
Institutions: Vanderbilt University Medical Center, University of Cambridge
Language: English
Subfield: Molecular Medicine
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4321493185