Predicting Antimicrobial Resistance Using Partial Genome Alignments.

ABSTRACT: Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 str

SUBMITTER: Aytan-Aktug D 

PROVIDER: S-EPMC8269213 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature