Author

Ichiro Takeuchi

Nagoya Institute of TechnologyORCID 0000-0002-1366-1946Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biophysics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 3 Assay:protein sequence· 3 Research Focus:protein folding & dynamics· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Keiichi InoueiD· 2 shared Masayuki KarasuyamaiD· 2 shared Chenxiang ZhaoiD· 1 shared Frederik SchulziD· 1 shared Hideaki E. KATOiD· 1 shared Hideki KandoriiD· 1 shared Hiromu YawoiD· 1 shared Hiroyuki Hanada· 1 shared

Activity by Year

2017-2025 · peak 1/yr

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10 top collaborators · 20 mutual co-author links

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2 shared papersBiophysics

Keiichi InoueORCID

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3 papers

Recent·Most cited

BiophysicsbioRxivPreprintSep 16, 2025

Functional characterization of red-shifted rhodopsin channels from giant viruses explored by a machine-learning model for long-wavelength optogenetics

Shunki Takaramoto, Chenxiang Zhao, Masayuki Karasuyama…·Keiichi Inoue

The paper uses a machine-learning model to predict red-shifted channelrhodopsin variants, identifying ChR024 with a maximum absorption wavelength of ~578 nm for optogenetics.

DOI 10.1101/2025.09.16.676488

protein sequencemachine learning (classical)

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BioinformaticsbioRxivPreprintAug 17, 2021

AllerStat: Finding Statistically Significant Allergen-Specific Patterns in Protein Sequences by Machine Learning

Kento Goto, Norimasa Tamehiro, Takumi Yoshida…·Ichiro Takeuchi

The paper introduces a machine learning method to identify statistically significant allergen-specific amino-acid sequences in proteins, achieving higher allergenicity prediction accuracy than existing approaches.

1 citation

protein sequencemachine learning (classical)

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BiophysicsbioRxivPreprintNov 29, 2017

Toward Machine Learning-based Data-driven Functional Protein Studies: Understanding Colour Tuning Rules and Predicting the Absorption Wavelengths of Microbial Rhodopsins

Masayuki Karasuyama, Keiichi Inoue, Hideki Kandori, Ichiro Takeuchi

The paper uses machine learning to predict and understand the absorption wavelengths of microbial rhodopsins by analyzing sequence and functional data.

DOI 10.1101/226118

protein sequencemachine learning (classical)

𝕏 PostBluesky