Work

Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.

Year: 2025

Type: preprint

Abstract: structural training, allowing us to predict which protein residues are most involved in the protein-nucleic acid binding interaction. Lastly, compared to single-omic controls trained with identical compute, OmniBioTE demonstrates superior performance-per-FLOP across both multi-omic and single-omic benchmarks, highlighting the power of a unified modeling approach for biological sequences.

Source: PubMed

DOI: 10.1371/journal.pone.0341501

Authors: Sully F Chen, Robert J Steele, Glen M Hocky, Beakal Lemeneh, Shivanand P Lad, Eric K Oermann

Institutions: Duke University, NYU Langone Health, New York University, Duke Medical Center

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: Harnessing simulations to uncover molecular mechanisms of mechanosensing

OpenAlex ID: W4409525657

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