Protein composite identifying method based on graph model

Abstract

The invention provides a protein composite identifying method based on a graph model; a protein interactive network of a given species is regarded as a network drawing =(V, E), wherein V is combining point of protein, and E is set of protein-protein interaction side; the self-connecting side and the repeat side in a network are removed from the set of all sides; the method includes steps of firstly acquiring a nuclear protein vertex set of the protein composite, and then expanding a first-order neighbor of the edge combining point thereof, and forming a graph model; judging its connectivity according to characteristics of the graph model, and finding out all dense subgraphs, namely, protein composite. The method provided by the invention can regard the graph model as the nucleus of the protein composite; through investigating and expanding the first-order neighbor combining point of the graph model, the protein composite is identified; an algorithm provided by the invention is applied to the known yeast protein network; the experimental result indicates that the algorithm can identify multiple protein composites with biological significance, and the algorithm is not sensitive to the input parameter.

Description

technical field [0001] The present invention relates to a protein complex recognition method, in particular to a protein complex recognition algorithm HPCMiner (H-index based Protein complex Miner) based on an H-index graph model, which regards the graph model H-index as a protein complex Kernel, protein complexes are identified by examining the first-order neighbor nodes that extend the graph model. Background technique [0002] In the post-gene era, systematically analyzing the topology of protein interaction networks and identifying protein complexes from protein networks plays an important role in predicting protein functions and explaining specific biological processes. At the same time, protein complex identification also provides a research approach for studying protein interaction networks. Protein networks can be represented by a graph model, where nodes are proteins and edges represent interactions between proteins. Such graphical models provide a new perspective...

AI Technical Summary

Problems solved by technology

[0005] In summary, the existing protein complex identification methods can identify few protein complexes with biological significance, and the algorithm is sensitive to input parameters, and the identification results are not ideal

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