Author

Ming Huang

Nara Institute of Science and TechnologyORCID 0000-0002-7833-7805Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Physiology· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 3 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:cell-cell communication· 1 Assay:electronic health records· 1 Assay:electrophysiology· 1 Assay:metabolomics· 1

Frequent Co-Authors

Naoaki OnoiD· 3 shared Shigehiko KanayaiD· 3 shared Koshiro Kido· 2 shared Md. Altaf-Ul-AminiD· 2 shared Altaf-UI-Amin· 1 shared Pei GaoiD· 1 shared Toshiyo Tamura· 1 shared Zheng CheniD· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 20 mutual co-author links

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3 shared papersBioinformatics

Naoaki OnoORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 16, 2021

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

Pei Gao, Ming Huang, Naoaki Ono…·Md. Altaf-Ul-Amin

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

DOI 10.1101/2021.06.16.444960

humanmetabolomics

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BioinformaticsbioRxivPreprintDec 25, 2019

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

Zheng Chen, Koshiro Kido, Naoaki Ono…·Ming Huang

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

DOI 10.1101/2019.12.25.888321

electronic health recordsmachine learning (classical)

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PhysiologybioRxivPreprintApr 17, 2019

The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals

Ming Huang, Koshiro Kido, Naoaki Ono…·Shigehiko Kanaya

This paper examines the feasibility of detecting atrial and ventricular arrhythmias using complexity metrics of heartbeat intervals, achieving 100% recall and precision with a 600-heartbeat-length signal.

DOI 10.1101/612002

humanelectrophysiology

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