Key protein identification method based on Markov random walk

Abstract

The invention aims to provide a key protein identification method based on Markov random walk, and belongs to the technical field of biological information. The key protein identification method basedon the Markov random walk comprises the following steps that: using a Markov random walk thought to endow each vertex with a score which expresses the importance degree of the vertex, giving the initial value of the score, and according to a certain probability, enabling the score to realize random walk, and carrying our revision in transfer, wherein the scores of all vertexes form an n-column vector; and finally, according to the descending order of the scores, outputting k pieces of protein corresponding to the scores as a final result. By use of the method, biological attributes and topological characteristics are combined to improve accuracy for identifying key protein, meanwhile, a prediction result is more accurate, and prediction efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of biological information, and mainly relates to a technology for identifying key proteins in a protein interaction network through a Markov random walk algorithm, in particular to a method for identifying key proteins in a PPI network based on network topology information and protein biological attributes. Background technique [0002] Protein is an indispensable substance in life activities. It participates in almost all cycles of life activities, and key proteins play an irreplaceable role in this process. The absence of key proteins may cause life organisms to fail to survive. Therefore, the identification of key proteins in the PPI network not only helps to understand the process of cell growth regulation, but also helps to study the mechanism of biological evolution. In addition, in the field of biomedicine, the identification of key proteins is of great significance in the treatment of diseases and th...

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Problems solved by technology

[0003] Before the present invention was proposed, the identification field of key proteins was initially identified by the topological characteristics of the network, for example, degree centrality (DC), betweenness centrality (BC), local average connectivity (LAC), Li, etc. People fused PPI and gene expression data and proposed the centrality measurement method PeC, Zhang et al. Fused PPI network topology features and gene co-expression information and proposed the CoEWC method, but the disadvantages of these methods to identify key proteins are: (1) only consider the network The topological characteristics of the protein itself, while ignoring the inherent biological characteristics of the protein

(2) There is noise in the PPI network obtained through biological experiments, which makes the protein interaction data have false positives

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