From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens.

ABSTRACT: Since the clinical introduction of antibiotics in the 1940s, antimicrobial resistance (AMR) has become an increasingly dire threat to global public health. Pathogens acquire AMR much faster than we discover new drugs (antibiotics), warranting innovative methods to better understand its molecular underpinnings. Traditional approaches for detecting AMR in novel bacterial strains are time-consuming and labor-intensive. However, advances in sequencing technology offer a plethora of bacterial genome data, and computational approaches like machine learning (ML) provide an optimistic scope for in silico AMR prediction. Here, we introduce a comprehensive multiscale ML approach to predict AMR phenotypes and identify AMR molecular features associated with a single drug or drug family, stratif

SUBMITTER: Ghosh A 

PROVIDER: S-EPMC12633328 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature