Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models.

ABSTRACT: The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive an

SUBMITTER: Yasir M 

PROVIDER: S-EPMC9280306 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature