Author

Shigehiko Kanaya

Nara Institute of Science and TechnologyORCID 0000-0002-2147-6900Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Plant Biology· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Organism:human· 1 Assay:metabolomics· 1

Frequent Co-Authors

Naoaki OnoiD· 3 shared Md. Altaf-Ul-AminiD· 2 shared Ming HuangiD· 2 shared Asahi AdachiiD· 1 shared John Jewish A. DomingueziD· 1 shared Kiwamu MinamisawaiD· 1 shared Koshiro Kido· 1 shared Masako Fuji· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBioinformatics

Naoaki OnoORCID

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3 papers

Recent·Most cited

Soil nutrition-dependent dynamics of the root-associated microbiome in paddy rice

Asahi Adachi, John Jewish A. Dominguez, Yuniar Devi Utami, Masako Fuji, Sumire Kirita, Shunsuke Imai, Takumi Murakami, Yuichi Hongoh, Rina Shinjo, Takehiro Kamiya, Toru Fujiwara, Kiwamu Minamisawa, Naoaki Ono, Shigehiko Kanaya, Yusuke Saijo

The paper examines how soil nutrition affects the root-associated microbiome of rice across seasons and conditions, finding nitrogen-fixing bacteria dominate in non-fertilized fields and a machine learning model can predict soil fertilization status from microbiome data.

Plant and Cell Physiology · May 21, 2025Peer-reviewedPlant Biology

Integrated Metabolomics to Reveal the Impacts of Common Antibiotics based on Drug Resistance Prediction of Gut Microbiota

Pei Gao, Ming Huang, Naoaki Ono, Shigehiko Kanaya, Md. Altaf-Ul-Amin

This study integrates metabolomics with machine learning to explore how antibiotics affect gut microbiota resistance phenotypes and their associated metabolites.

bioRxiv · Jun 16, 2021 · PreprintBioinformatics

The Feasibility of Predicting Impending Malignant Ventricular Arrhythmias on the basis of Signal Complexity of Heartbeat Intervals

Zheng Chen, Koshiro Kido, Naoaki Ono, Md. Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang

This paper proposes a method using heartbeat interval complexity metrics and machine learning to predict impending malignant ventricular arrhythmias with high accuracy.

bioRxiv · Dec 25, 2019 · PreprintBioinformatics