Correlation module identifying method based on 2-type heterogeneous network

A heterogeneous network and recognition method technology, applied in the field of computer data processing, can solve problems such as bipartite graph model limitations and data incompleteness, core interaction relationship mining and new interaction relationship prediction deviations, etc., to achieve a wide range of applications , the result is accurate and reliable

Active Publication Date: 2013-04-10
INST OF AUTOMATION CHINESE ACAD OF SCI
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the deviations in the mining of the core interaction relationship between the two types

Method used

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  • Correlation module identifying method based on 2-type heterogeneous network
  • Correlation module identifying method based on 2-type heterogeneous network
  • Correlation module identifying method based on 2-type heterogeneous network

Examples

Experimental program
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Embodiment

[0065] Example: Mining the core correlation module of the molecular network between the formula Maxing Shigan-Yinqiao San and H1N1 influenza.

[0066] Also refer to image 3 This embodiment of the present invention will be described.

[0067] S0, preprocessing

[0068] By consulting experts in traditional Chinese medicine and searching literature, the 12 Chinese herbal medicines and the chemical components contained in the formula of Maxing Shigan-Yinqiao powder were obtained. The 12 kinds of Chinese medicinal materials are: licorice, roasted ephedra, artemisia annua, silver flower, scutellaria, forsythia, mint, fried almonds, burdock seeds, anemarrhena, fritillaria, and gypsum. There are 449 compounds included in the PubChem database. ADMET Predictor was used to evaluate the drug-likeness of the compounds, and the compounds with high ADMET risk were eliminated. Finally, 344 drug-like chemical constituents were retained.

[0069] Through the KEGG Pathway database, 174 gen...

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Abstract

The invention discloses a correlation module identifying method based on a 2-type heterogeneous network. The method comprises the following steps of S1, according to information of individuals in two types and a mutual action relationship between the individuals of the two types, establishing the 2-type heterogeneous network; S2, according to a topology structure of the 2-type heterogeneous network, establishing a node topology vector; S3, adopting a hierarchical clustering method based on a margin strategy, and dividing the 2-type heterogeneous network into a plurality of modules by a particular evaluating function; and S4, removing the invalid correlation modules from the optimum division of the network. The method has the advantages that the actual and potential network interaction nodes and interaction relationships are comprehensively considered, a core interaction mode of the network containing the individuals of the two types is mined more efficiently, and the mining result is accurate and reliable.

Description

technical field [0001] The invention belongs to the field of computer data processing, and in particular relates to the application and expansion of complex network theory in data mining, especially its method for identifying main correlation modules in 2-type heterogeneous networks. Background technique [0002] With the advent of the big data era, a large amount of unstructured data floods various research fields. To extract information or learn knowledge from such data, the first problem is how to establish a model to describe the data structure. Among them, a special graph - network, can represent a large amount of interrelated data to a certain extent. The complex network theory developed on the network model has been applied in many fields to solve various problems. The proposal and development of complex networks play an important role in knowledge acquisition in the era of big data. [0003] In practical application problems, a type of problem that is often involv...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 卢朋宋江龙高一波陈琳刘西代文陈迪
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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