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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 417-424
Chunyang Ruan , Shanghai key Laboratory of Data Science, School of Computer Scinece Fudan University, Shanghai, China
Ye Wang , College of Engineering and Science, Victoria University, Melbourne, Australia
Yanchun Zhang , Shanghai key Laboratory of Data Science, School of Computer Scinece Fudan University, Shanghai, China
Jiangang Ma , College of Engineering and Science, Victoria University, Melbourne, Australia
Huijuan Chen , School of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
Uwe Aickelin , School of Computer Science, The University of Nottingham, Ningbo, China
Shanfeng Zhu , Shanghai key Laboratory of Data Science, School of Computer Scinece Fudan University, Shanghai, China
Ting Zhang , School of Computer Scinece Fudan University, Shanghai, China
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.
INDEX TERMS
Conferences, Bioinformatics
CITATION

C. Ruan et al., "THCluster: Herb supplements categorization for precision traditional Chinese medicine," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 417-424.
doi:10.1109/BIBM.2017.8217685
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