Abstract
Traditional Chinese Medicine (TCM) is a holistic approach to medical treatment. Analysis and decision cannot be made in isolation, hence, the extraction of symptoms-herbs relationship is a crucial step to the research of the underlying TCM principle. Since this kind of relationship bears a lot of similarity with the gene-expression study in the microarray analysis, where the use of biclustering algorithms is common, it is logical to apply biclustering algorithms to the study of symptom-herb relationship. However, the choice of feature representation is a dominant factor in the success of any machine learning problem. This paper aims to understand the impact of different representation schemes in the biclustering of symptoms-herbs relationship. A bicluster is not helpful if the number of features is too large or too small. In order to get a desirable size for the biclusters, modified relative success ratio is considered to be the most appropriate one among the other four schemes. Some of the biclusters (using modified relative success ratio) do follow the therapeutic principle of TCM, while some biclusters with interesting feature combination that are worthwhile for clinical evaluation.
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References
Hsiao, C.F., Tsou, H.H., Wu, Y.J., Lin, C.H., Chang, Y.J.: Translation in different diagnostic procedures—traditional Chinese medicine and Western medicine. Journal of the Formosan Medical Association 107(12 suppl.), 74–85 (2008)
Feng, Y., Wu, Z., Zhou, X., Zhou, Z., Fan, W.: Knowledge discovery in traditional Chinese medicine: state of the art and perspectives. Artificial Intelligence in Medicine 38(3), 219–236 (2006)
Zhou, X., Chen, S., Liu, B., Zhang, R., Wang, Y., Li, P., Guo, Y., Zhang, H., Gao, Z., Yan, X.: Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artificial Intelligence in Medicine 48(2-3), 139–152 (2010)
Ung, C.Y., Li, H., Cao, Z.W., Li, Y.X., Chen, Y.Z.: Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties. Journal of Ethnopharmacology 111(2), 371–377 (2007)
Yan, X., Milne, G.W.A., Zhou, J., Xie, G.: Traditional Chinese medicines: molecular structures, natural sources, and applications. Ashgate, Aldershot (1999)
Shao, L.: Network Systems Underlying Traditional Chinese Medicine Syndrome and Herbs Formula. Current Bioinformatics 4, 188–196 (2009)
Cheng, Y., Church, G.: Biclustering of Expression Data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 93–103. AAAI Press (2000)
Tanay, A., Sharan, R., Shamir, R.: Discovering Statistically Significant Biclusters in Gene Expression Data. Bioinformatics 18, S136–S144 (2002)
Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y., Barkai, N.: Revealing Modular Organization in the Yeast Transcriptional Network. Nature Genetics 31, 370–377 (2002)
Ihmels, J., Bergmann, S., Barkai, N.: Defining Transcription Modules Using Large-Scale Gene Expression Data. Bioinformatics 20, 1993–2003 (2004)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999)
Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data. Bioinformatics 22(9), 1122–1129 (2006)
Zhou, X., Poon, J., Kwan, P., Zhang, R., Wang, Y., Poon, S., Liu, B., Sze, D.: Novel Two-stage Analytic Approach in Extraction of Strong Herb-herb Interactions in TCM Clinical Treatment of Insomnia. In: International Conference on Medical Biometrics (ICMB 2010), pp. 28–30 (2010)
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Poon, S., Luo, Z., Zhang, R. (2012). The Impact of Feature Representation to the Biclustering of Symptoms-Herbs in TCM. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_33
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DOI: https://doi.org/10.1007/978-3-642-28320-8_33
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