Method for identifying key proteins in protein-protein interaction network

Abstract

The invention discloses a method for identifying key proteins in a protein-protein interaction network. According to the method, an undirected graph G is constructed according to the protein-protein interaction data, and the edge clustering coefficient of the graph is calculated. Compared with the prior art, the method provided by the invention has the advantages of combining the gene expression profile data and the gene function annotation information data on the basis of considering the topological structure characteristics of the protein-protein interaction network, and integrating three groups of data to predict the key proteins, so that the influence caused by the data noise of a single data source on the prediction correctness can be effectively decreased, and the key proteins in the network can be predicted through the key protein characteristics embodied by three types of data, such as the edge clustering coefficient in the protein-protein interaction network, the Pearson's correlation coefficient of the gene expression value and the gene function similarity index. According to the method, the identification correctness of the key proteins in the protein-protein interaction network can be remarkably improved, and abundant key proteins can be predicted once, so that the problem that the biological experiment method is high in cost and time-consuming is solved.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a method for identifying key proteins in protein interaction networks. Background technique [0002] Protein is the scaffold and main substance that constitute biological tissues and organs. It is the executor of physiological functions and plays a very important role in life activities. Key proteins play a vital role in maintaining the normal physiological process of organisms. Once these proteins are removed, the biological functions of related protein complexes and functional modules will be lost, resulting in the inability of organisms to complete normal physiological activities, and ultimately lead to biological Physiological disorders or death. Effectively predicting key proteins has very important biological significance for studying the physiological regulation mechanism of cells, and also has very important practical value for drug target design. [0003] In the field of ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for identifying key proteins in a protein interaction network, by calculating the edge clustering coefficient of the protein interaction network, the Pearson correlation coefficient of gene expression values ​​and the gene function similarity index To describe the characteristics of key proteins at the level, and effectively combine these three characteristics to predict key proteins. The present invention does not need to rely on existing key protein information, and has high accuracy, effectively solving the problems of expensive cost and long time period of biological experiments.

Images