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. 2013:2013:971272.
doi: 10.1155/2013/971272. Epub 2013 Oct 30.

Application of genetic algorithm for discovery of core effective formulae in TCM clinical data

Ming Yang  1 , Josiah Poon, Shaomo Wang, Lijing Jiao, Simon Poon, Lizhi Cui, Peiqi Chen, Daniel Man-Yuen Sze, Ling Xu
Affiliations

Application of genetic algorithm for discovery of core effective formulae in TCM clinical data

Ming Yang et al. Comput Math Methods Med. 2013.

Abstract

Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.

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Figures

Figure 1
Figure 1
An illustrative example for data format and SCV calculation.
Figure 2
Figure 2
Data format for combinatorial optimization problem of discovery of CEF.
Figure 3
Figure 3
Flow chart of GA and explanations of the sequence of GA steps for the discovery of CEF.
Figure 4
Figure 4
Parameter selection in GA.
Figure 5
Figure 5
Fitness value by GA with different N and S 1 combination.
Figure 6
Figure 6
Distribution of SI.
See this image and copyright information in PMC

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