A method for identifying key proteins in protein interaction networks

Abstract

The invention discloses a method for identifying key proteins in a protein interaction network. According to the protein interaction data, an undirected graph G is constructed, and the edge clustering coefficient of the graph is calculated. Compared with the existing technology, the invention considers protein Based on the topology characteristics of the interaction network, combined with gene expression profile data and gene function annotation information data, integrating three sets of data to predict key proteins can effectively reduce the impact of single data source data noise on prediction accuracy. The key protein characteristics reflected in the three types of data, the edge clustering coefficient of the interaction network, the Pearson correlation coefficient of the gene expression value, and the gene function similarity index, are combined to predict the key protein in the network, and the present invention can significantly improve the protein interaction network. The recognition accuracy of the key proteins in the medium, and a large number of key proteins can be predicted at one time, which solves the expensive cost and time-consuming problems of biological experiment methods.

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 material that constitutes biological tissues and organs. It is the performer of physiological functions and plays a very important role in life activities. Key proteins play a vital role in maintaining the normal physiological processes of the organism. Once these proteins are removed, the biological functions of the related protein complexes and functional modules will be lost, resulting in the organism’s inability to complete normal physiological activities, and ultimately lead to biological Physical disorders or death. Effectively predicting key proteins has very important biological significance for studying the physiological regulation mechanism of cells, and also has important practical value for drug target design. [0003] In the field of biology, ...

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.

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