Author

Hideki Yamaguchi

Artificial Intelligence Research CenterORCID 0000-0001-9096-5591Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein sequence· 2 Research Focus:variant effect prediction· 2 Approach:transformer / foundation model· 1

Frequent Co-Authors

Yutaka SaitōiD· 2 shared Adrien MiallandiD· 1 shared Riku Katsuki· 1 shared Shuzo Fukunaga· 1 shared Yunfei Dong· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersBioinformatics

Yutaka SaitōORCID

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJun 17, 2026

Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation

Adrien Mialland, Shuzo Fukunaga, Riku Katsuki…·Yutaka Saitō

This paper introduces fitness translocation, a data augmentation method for variant effect prediction that improves model performance using homologous protein fitness data.

1 citation

protein sequencedeep learning

𝕏 PostBluesky

BioinformaticsBriefings in BioinformaticsPeer-reviewedJun 2, 2021

Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins

Hideki Yamaguchi, Yutaka Saitō

This paper introduces evotuning protocols for Transformer-based variant effect prediction, showing improved performance by incorporating domain architecture information from homologous sequences.

15 citations

protein sequencedeep learning

𝕏 PostBluesky