Chinese medicine formula network analysis for core herbal discovery

  • Authors:

  • Gao Yuan;Liu Zheng;Wang Chong-Jun;Fan Xin-Sheng;Xie Jun-Yuan

  • Affiliations:

  • National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China, Department of Computer Science and Technology, Nanjing University, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China, Department of Computer Science and Technology, Nanjing University, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China, Department of Computer Science and Technology, Nanjing University, Nanjing, China;Jiangsu Key Laboratory for Modern Traditional Chinese Medicine Formulae, Nanjing University of Traditional Chinese Medicine, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China, Department of Computer Science and Technology, Nanjing University, Nanjing, China

  • Venue:
  • BI'12 Proceedings of the 2012 international conference on Brain Informatics
  • Year:
  • 2012

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Abstract

Data mining is a hotspot in the traditional Chinese medical (TCM) field now. Because it glosses over the relation between herbals, the traditional Chinese medical formula (CMF) data organization method, in which different records are concerned different CMFs, cannot meet the need for deep data analysis. This paper proposes an effective approach for CMF networking according to the Jaccard similarity coefficient; then we carried out an analysis of the CMF network features which shows the CMF network has properties of complex network. Meanwhile, an algorithm for core herbal discovery is presented basing on key nodes discovery method and the MapReduce [1] parallel programming framework. The result indicates the feasibility of our ideas and the validity of the algorithm.