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THCluster: herb supplements categorization for precision traditional Chinese medicine

Ruan, Chunyang; Wang, Ye; Zhang, Yanchun; Ma, Jiangang; Chen, Huijuan; Aickelin, Uwe; Zhu, Shanfeng; Zhang, Ting

Authors

Chunyang Ruan

Ye Wang

Yanchun Zhang

Jiangang Ma

Huijuan Chen

Uwe Aickelin

Shanfeng Zhu

Ting Zhang



Abstract

There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization (EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.

Publication Date Nov 13, 2017
Journal 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA
Peer Reviewed Peer Reviewed
APA6 Citation Ruan, C., Wang, Y., Zhang, Y., Ma, J., Chen, H., Aickelin, U., …Zhang, T. (2017). THCluster: herb supplements categorization for precision traditional Chinese medicine
Keywords Herb categorization, Heterogeneous information network, Clustering
Publisher URL http://ieeexplore.ieee.org/abstract/document/8217685/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway, N.J. : IEEE, c2017. Electronic ISBN: 978-1-5090-3050-7 pp. 417-424, doi:10.1109/BIBM.2017.8217685 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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