Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

ABSTRACT: It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors foun

SUBMITTER: Pataki BA 

PROVIDER: S-EPMC7490380 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature