Author

Yiming Zhang

The University of TokyoORCID 0009-0000-8730-4047Unclaimed ORCID profile · follow to get notified of new preprints

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Yiming Zhang6 papers · The University of Tokyo

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:large language model· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Research Focus:software / tool· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Koji TsudaiD· 1 shared Manabu OkumuraiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

2 top collaborators · 0 mutual co-author links

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1 shared paperBioinformatics

Koji TsudaORCID

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2 papers

Recent·Most cited

BioinformaticsIEEE International Conference on Bioinformatics and Biomedicine (BIBM)Peer-reviewedDec 15, 2025

Ab-VS: Evaluating Large Language Models for Virtual Antibody Screening via Antibody-Antigen Interaction Prediction

Yiming Zhang, Koji Tsuda

Ab-VS-Bench evaluates large language models for antibody virtual screening by predicting antibody-antigen interactions through three tasks: scoring, ranking, and screening.

DOI 10.1109/bibm66473.2025.11357211

large language modelbenchmarking

𝕏 PostBluesky

BioinformaticsbioRxivPreprintJan 18, 2024

ProtHyena: A fast and efficient foundation protein language model at single amino acid resolution

Yiming Zhang, Manabu Okumura

ProtHyena is a foundation protein language model that efficiently processes long protein sequences at the single amino acid level with reduced computational complexity.

5 citations

protein sequencedeep learning

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