Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae.

ABSTRACT: Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based pr

SUBMITTER: Batisti Biffignandi G 

PROVIDER: S-EPMC10995625 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature