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Organizational overlapping core drug group discovery method based on complex network community discovery

A technology of community discovery and complex network, applied in the field of hierarchical overlapping core drug group discovery

Inactive Publication Date: 2012-09-19
HOHAI UNIV
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Problems solved by technology

[0005] In order to further reveal the "complexity" of the theoretical system of traditional Chinese medicine and the "implicitness" of the prescription compatibility law, we use the complex network model to explore the prescription compatibility law from the perspective of complex network community discovery, and propose a new real complex network , Traditional Chinese Medicine Formula (TCMF for short) network, due to the evolution of the formula "single formula -> basic formula -> compound formula" and the existence of addition and subtraction factors, TCMF network is a high-level network that is different from traditional complex networks. Overlapping networks not only have overlapping nodes, but also overlapping edges. Traditional overlapping community discovery algorithms are not suitable for TCMF networks. Therefore, we propose an overlapping community discovery algorithm for this network

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  • Organizational overlapping core drug group discovery method based on complex network community discovery
  • Organizational overlapping core drug group discovery method based on complex network community discovery
  • Organizational overlapping core drug group discovery method based on complex network community discovery

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[0070] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0071] Such as figure 1 As shown, the input data source of the TCMF network prescription mining visualization system is the database of traditional Chinese medicine prescriptions. By constructing the TCMF network and discovering the TCMF network, the overlapping core medicine group is obtained. There is a high possibility of compatibility and combination within the medicine group. There are three parameters here. The settings are the occurrence contribution threshold min_ac, the high occurrence contribution low correlation threshold max_ac, and the medicine group community distance threshold λ.

[0072] Such as figure 2 As shown, the hierarchical overlapping core drug group discovery method based on complex network community discovery of the present invention mainly includes two steps, constructing TCMF network and TCMF network discovery, specifically as follows: ...

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Abstract

The invention provides an organizational overlapping core drug group discovery method based on complex network community discovery, which comprises the following steps: 1) the construction of a traditional Chinese medical formula (TCMF) network: a) the appear contribution (AC) of each drug in a given amount of formula is calculated, and the drugs of which the AC is smaller than a certain threshold value are deleted; b) AC values of two-tuple components are calculated to be cut, and then AC values of triple drugs are calculated to be cut; c) the TCMF network is constructed by using the rest triple drugs; d) the step 1) is completed; and 2) organizational overlapping core drug group discovery of the TCMF network: a) the TCMF network obtained in the step 1) is pre-processed; b) a max clique growing algorithm (MAIGA) is executed on the pre-processed TCMF network, so as to discover drug groups; c) a drug group division result is returned; and d) the step 2) is completed. The organizational overlapping core drug group discovery method explores the compatibility law of the TCMF a viewpoint of complex network, provides a new model for the research of TCMF mining, and solves the problem of traditional single distribution of a cluster.

Description

technical field [0001] The invention relates to a method for constructing a traditional Chinese medicine formula (TCMF) network and a method for discovering a layered overlapping core medicine group applicable to the TCMF network. Background technique [0002] At present, there are three main modes of research on the compatibility of prescriptions using data mining technology: classification-based data mining research mode, cluster-based data mining research mode and association rule mining-based research mode. These three models mostly focus on the analysis of local information such as drug pairs, drug groups or drug-symptoms, syndrome-symptom correlations, etc., and there are still some deficiencies in revealing the "complexity" of the theoretical system of traditional Chinese medicine and the "implicitness" of prescription compatibility laws. For example, the data mining research model based on clustering is difficult to solve the single assignment problem of drug (or pre...

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Application Information

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IPC IPC(8): G06F17/50G06F17/30
Inventor 吴骏孙道平许峰王志坚
Owner HOHAI UNIV
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