DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

ABSTRACT:

Background

Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefits of antibiotics. Therefore, early detection of antimicrobial resistance genes (ARGs), especially in pathogens, is crucial for human health. Most computational methods for ARG detection rely on homology to a predefined gene database and therefore are limited in their ability to discover novel genes.

Results

We introduce DRAMMA, a machine learning method for predicting new ARGs with no sequence similarity to known ARGs or any annotated gene. DRAMMA utilizes various features, including protein properties, genomic context, and evolu

SUBMITTER: Rannon E 

PROVIDER: S-EPMC11887096 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature