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Creators/Authors contains: "Mollerus, Matthew"

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  1. ; ; ; (, bioRxiv)
    Abstract The pace of antibiotic resistance necessitates advanced tools to detect and analyze antibiotic resistance genes (ARGs). We presentresLens, a family of genomic language models (gLM) leveraging latent genomic representations for ARG detection and analysis. Unlike alignment-based methods constrained by reference databases,resLensfine-tunes pre-trained gLMs on curated ARG datasets, achieving superior performance across several evaluation scenarios, including when ARGs exhibit dissimilar sequences and mechanisms to those in reference databases. 
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    Free, publicly-accessible full text available July 11, 2026