Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus.

ABSTRACT: Introduction: Staphylococcus aureus is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited. Methods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those no

SUBMITTER: Khabaz H 

PROVIDER: S-EPMC10544327 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature