Author

Matthew Mollerus

George Washington UniversityORCID 0009-0001-6393-8739Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Ali RahnavardiD· 2 shared Keith A. CrandalliD· 2 shared Katharina DittmariD· 1 shared Vedant MahangadeiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

4 top collaborators · 5 mutual co-author links

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2 shared papersBioinformatics

Ali RahnavardORCID

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2 papers

Recent·Most cited

Bioinformaticsnpj Antimicrobials and ResistancePeer-reviewedMay 21, 2026

resLens: genomic language models to enhance antibiotic resistance gene detection

Matthew Mollerus, Katharina Dittmar, Keith A. Crandall, Ali Rahnavard

resLens improves antibiotic resistance gene detection by using genomic language models trained on curated datasets, outperforming reference-based methods in varied scenarios.

6 citationstop 1%

whole-genome sequencingdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintFeb 18, 2026

Guided tokenization and domain knowledge enhance genomic language models' performance

Vedant Mahangade, Matthew Mollerus, Keith A. Crandall, Ali Rahnavard

This paper introduces guided tokenization and domain adaptation to improve genomic language models' performance in classification and annotation tasks.

1 citation

DNA methylationwhole-genome sequencing

𝕏 PostBluesky