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The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm

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Book cover Biomedical Data Management and Graph Online Querying (Big-O(Q) 2015, DMAH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9579))

Abstract

One of the major research contents carried by scholars of Traditional Chinese medical science (TCM) is to discover the compatibility rules of herbs to increase the efficacy in treating certain syndromes. However, up to now, most of the compatibility rules of herbs are based on empirical analyses, which make them hard to study. Since concepts of Big Data and machine learning have been popularized gradually, how to use data mining techniques to effectively figure out core herbs and compatibility rules becomes the main research aspect of TCM informatics. In this paper, the hypergraph partitioning algorithm HMETIS based on Apriori is applied to exploit and analyze clinical data about lung cancer. The result shows that all 15 Chinese herbs obtained by the algorithm accord with the core concepts of the treatment of lung cancer by experienced TCM doctors, namely replenishing nutrition, clearing heat-toxin, resolving phlegm and eliminating pathogenic factors.

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References

  1. Yejun, C.: Data mining Technology and its application in Traditional Chinese Medicine. Zhejiang University (2003)

    Google Scholar 

  2. Zhou, X., Liu, Y., et al.: Research on compound drug compatibility of complex network. Chin. J. Inf. Tradit. Chin. Med. 15(11), 98–100 (2008)

    Google Scholar 

  3. Meng, F., Li, M., et al.: Mining the medication law of ancient analgesic formulas based on complex network. J. Tradit. Chin. Med. 54(2), 145–148 (2013)

    MathSciNet  Google Scholar 

  4. Zhang, B.: Research on data-mining technology applied traditional Chinese prescription compatibility based on association rules. J. Gansu Lianhe Univ. (Nat. Sci.) 25(1), 82–86 (2011)

    Google Scholar 

  5. Zhou, X., et al.: Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif. Intell. Med. 48(2), 139–152 (2010)

    Article  Google Scholar 

  6. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, vol. 1. Pearson Addison Wesley, Boston (2006)

    Google Scholar 

  7. Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)

    Article  Google Scholar 

  8. Han, J., Kamber, M., Pei, J.: Data Mining, Southeast Asia Edition: Concepts and Techniques. Morgan kaufmann, San Francisco (2006)

    MATH  Google Scholar 

  9. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference Very Large Data Bases VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  10. Tseng, V.S., et al.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013)

    Article  Google Scholar 

  11. Shang, J., Lisheng, H., et al.: Data mining of the law of compatibility of medicines and application of banxia xiexin decoction. J. China-Jpn. Friendship Hosp. 19(4), 227–229 (2005)

    Google Scholar 

  12. Ye, L., Fan, X., et al.: Association among four-drug decoction and the like for dysmenorrhea at all times. J. Nanjing Univ. Tradit. Chin. Med. (Nat. Sci.) 24(2), 94–96 (2008)

    Google Scholar 

  13. Zhou, X., Liu, B.: Network analysis system for traditional Chinese medicine clinical data. In: 2nd International Conference on Biomedical Engineering and Informatics, BMEI 2009, pp. 1–5. IEEE (2009)

    Google Scholar 

  14. Zhang, R., Zhou, X., et al.: Study on compounding rules of Chinese herb prescriptions for treating syndrome of liver and spleen disharmony by scale-free network. World Sci. Technol.-Modernization Tradit. Chin. Med. 12(6), 882–887 (2010)

    Google Scholar 

  15. Yu, J., Tao, D., Wang, M.: Adaptive hypergraph learning and its application in image classification. IEEE Trans. Image Process. 21(7), 3262–3272 (2012)

    Article  MathSciNet  Google Scholar 

  16. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)

    Article  MATH  Google Scholar 

  17. Fiduccia, C.M, Robert M.M.: A linear-time heuristic for improving network partitions. In: 19th Conference on Design Automation. IEEE (1982)

    Google Scholar 

  18. Vazquez, A.: Finding hypergraph communities: a bayesian approach and variational solution. J. Stat. Mech. Theor. Exp. (2009)

    Google Scholar 

  19. Chakraborty, A., Saptarshi, G.: Clustering hypergraphs for discovery of overlapping communities in folksonomies. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds.) Dynamics on and of Complex Networks, vol. 2, pp. 201–220. Springer, New York (2013)

    Google Scholar 

  20. Bulò, S.R., Marcello, P.: A game-theoretic approach to hypergraph clustering. Adv. Neural Inf. Process. Syst. 35, 1571–1579 (2009)

    Google Scholar 

  21. Li, Y.: An entropy-based algorithm for detecting overlapping communities in hyper-networks. Sci. Technol. Eng. 13(7), 1856–1859 (2013)

    Google Scholar 

  22. Han, E.-H., et al.: Clustering in a high-dimensional space using hypergraph models. In: Proceedings of Data Mining and Knowledge Discovery (1997)

    Google Scholar 

  23. Church, K.W., Patrick, H.: Word association norms, mutual information, and lexicography. Comput. linguist. 16(1), 22–29 (1990)

    Google Scholar 

  24. Lin, X.: Experience of Professor Xixiang Liu in treating lung cancer. Inf. Tradit. Chin. Med. 12(4), 36–37 (1995)

    Google Scholar 

  25. Liu, J., Pan, M., et al.: Clinical study of Jin Hu Kang oral liquid for treating non-small cell lung cancer. Tumor 21(6), 463–465 (2001)

    Google Scholar 

  26. Ji, W.: Professor LIU Jia-xiang’s experience in the treatment of lung cancer with Chinese drug pair. Chin. Arch. Tradit. Chin. Med. 28(6), 1154–1156 (2010)

    Google Scholar 

  27. Shiyun, Z., Jinnan, Z.: Experimental study on treatment of compound hedyotic diffusa in tumor-burdened mice. Pract. Clin. J. Integr. Tradit. Chin. West. Med. 09, 81–83 (2014)

    Google Scholar 

  28. Wang, P., Zhang, S.: Research advances of the anticancer mechanisms of prunella vulgaris. Shandong Sci. 23(2), 38–41 (2010)

    Google Scholar 

  29. Yan, P.: Traditional Chinese Medicine Dispensing Technology. Chemical industry press (2006)

    Google Scholar 

  30. The State Administration of Traditional Chinese Medicine《Zhonghua Bencao》.Shanghai: Shanghai science and technology publishing house (1999)

    Google Scholar 

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Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61301028); Natural Science Foundation of Shanghai China (Grant No. 13ZR1402900); Doctoral Fund of Ministry of Education of China (Grant No. 20120071120016).

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Authors and Affiliations

  1. Shanghai University of T.C.M., No. 1200, Cailun Road, Shanghai, 201210, China

    Miao Wang, Qiang Zhou & Lijuan Che

  2. Department of Electronic Engineering, Fudan University, No. 220, Handan Road, 200433, Shanghai, China

    Jiayun Li, Li Chen, Yanjun Huang & Huiliang Shang

Authors
  1. Miao Wang
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  2. Jiayun Li
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  3. Li Chen
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  4. Yanjun Huang
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  5. Qiang Zhou
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  6. Lijuan Che
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  7. Huiliang Shang
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Corresponding author

Correspondence to Huiliang Shang .

Editor information

Editors and Affiliations

  1. Stony Brook University, Stony Brook, New York, USA

    Fusheng Wang

  2. University of Utah, Salt Lake City, Utah, USA

    Gang Luo

  3. Columbia University, New York, New York, USA

    Chunhua Weng

  4. Nanyang Technological University, Singapore, Singapore

    Arijit Khan

  5. Qatar Computing Research Institute, Doha, Qatar

    Prasenjit Mitra

  6. Google Research, New York, New York, USA

    Cong Yu

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Wang, M. et al. (2016). The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-41576-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41575-8

  • Online ISBN: 978-3-319-41576-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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