A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction in streptococcus pneumoniae.

ABSTRACT: Streptococcus pneumoniae is one of the major concerns of clinicians and one of the global public health problems. This pathogen is associated with high morbidity and mortality rates and antimicrobial resistance (AMR). In the last few years, reduced genome sequencing costs have made it possible to explore more of the drug resistance of S. pneumoniae, and machine learning (ML) has become a popular tool for understanding, diagnosing, treating, and predicting these phenotypes. Nucleotide k-mers, amino acid k-mers, single nucleotide polymorphisms (SNPs), and combinations of these features have rich genetic information in whole-genome sequencing. This study compares different ML models for predicting AMR phenotype for S. pneumoniae. We compared nucleotide k-mers, amino acid k-mers, SNPs, and the

SUBMITTER: Kaya DE 

PROVIDER: S-EPMC11731958 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature