Author

Nima Azbijari

Oregon State University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:metagenomics· 2 Research Focus:protein structure prediction· 2 Approach:transformer / foundation model· 2 Approach:contrastive learning· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Andrew R. ThurberiD· 2 shared Jacob H. WynneiD· 2 shared Maude M. DavidiD· 2 shared Kristopher Irizarry· 1 shared Tanush Sistla· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersBioinformatics

Andrew R. ThurberORCID

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2 papers

Recent·Most cited

Metagenomic contextualization of proteins with state space models

Nima Azbijari, Jacob H. Wynne, Kristopher Irizarry, Tanush Sistla, Andrew R. Thurber, Maude M. David

This paper introduces Nammu, a state-space model for metagenomic data that outperforms Transformer-based models in taxonomic and functional predictions.

bioRxiv · Jul 11, 2026 · PreprintBioinformatics

BioGeoFormer: A deep learning approach to classify unknown genes associated with critical biogeochemical cycles

Jacob H. Wynne, Nima Azbijari, Andrew R. Thurber, Maude M. David

This paper introduces BioGeoFormer, a protein language model that classifies unknown genes into biogeochemical pathways, outperforming traditional methods in identifying functional roles in deep-sea microbial genes.

bioRxiv · Dec 18, 2025 · PreprintMicrobiology