Author

Teppei Deguchi

Artificial Intelligence Research CenterORCID 0009-0001-6340-3750Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Kaito KobayashiiD· 1 shared Kento TominagaiD· 1 shared Kimiho OmaeiD· 1 shared Koji HamasakiiD· 1 shared Nur Syatila Ab GhaniiD· 1 shared Satoshi NishinoiD· 1 shared Shinji IidaiD· 1 shared Susumu YoshizawaiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Kaito KobayashiORCID

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2 papers

Recent·Most cited

MicrobiologybioRxivPreprintDec 12, 2025

Functional Unknomics of the SAR11 clade using bioinformatics approaches

Satoshi Nishino, Kento Tominaga, Kimiho Omae…·Yuki Nishimura

This study investigates the function of the unknome in SAR11 clade bacteria, identifying genes involved in substrate uptake, nucleotide utilization, and antiviral defense, providing insights into their ecological success.

1 citation

proteomicsstatistical model

𝕏 PostBluesky

BioinformaticsBriefings in BioinformaticsPeer-reviewedAug 31, 2025

Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models

Teppei Deguchi, Nur Syatila Ab Ghani, Yoichi Kurumida…·Yutaka Saitō

The paper presents a method that improves protein mutational effect prediction with limited experimental data by combining molecular simulation and protein language models.

2 citations

protein sequencedeep learning

𝕏 PostBluesky