resLens: genomic language models to enhance antibiotic resistance gene detection
Abstract
The rise of antibiotic resistance necessitates advanced tools to detect and analyze antibiotic resistance genes (ARGs). We present resLens, a family of genomic language models that leverage latent genomic representations to enhance ARG detection and analysis. Unlike alignment-based methods constrained by reference databases, resLens fine-tunes a pre-trained DNA language model on curated ARG datasets, achieving competitive or superior performance in classifying resistance genes across multiple evaluation scenarios, including when ARGs exhibit sequences and mechanisms of resistance dissimilar to those in reference datasets.
Fields of Study & Subjects
Antibiotic Resistance in Bacteria
Pharmaceutical and Antibiotic Environmental Impacts
Genomics and Phylogenetic Studies
Publication details
- Journal / source
- npj Antimicrobials and Resistance
- Publisher
- Springer Science and Business Media LLC
- Volume
- 4
- Issue
- 1
- Publication date
- 21 May 2026
- Language
- EN