Protein function module identification method for label propagation algorithm on the basis of edge driving

Abstract

The invention discloses a protein function module identification method for a label propagation algorithm on the basis of edge driving. The protein function module identification method comprises thefollowing steps: S1, randomly distributing a unique integral value m as the tag of an edge to an absolute value E of edges in a PPI (Protein-Protein Interaction) network; S2, according to a defined label update rule, through iteration, changing the label of each edge in the PPI network until the label in the network is not changed. According to the protein function module identification method, the interference of noise in the PPI network can be reduced, the stability of an algorithm identification process is improved, and therefore, a more accurate and effective protein function module division result is obtained in the larger-scale PPI network.

Description

technical field [0001] The present invention relates to the technical field of complex protein network functional module recognition, specifically a protein functional module recognition method based on edge-driven label propagation algorithm, by describing the PPI network as an undirected graph, using the label propagation algorithm to identify the protein functional modules. Background technique [0002] In recent years, with the rapid development of high-throughput biological experiment methods, a large number of networks reflecting the interactions between all proteins in living organisms have been formed. How to understand the hidden biological significance is a very important research content in the post-gene era . On the one hand, protein, as an important part of all cells and tissues in the body, plays an important role in various life activities of human beings; on the other hand, a complex life activity is not assisted by a single protein, but requires Multiple d...

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Problems solved by technology

The algorithm utilizes the information of local nodes in the identification process. However, if there is a division error in the level of a certain node, it will cause errors in the subtrees under it. Therefore, this type of method is sensitive to network noise.

[0007] 3) Partition-based clustering algorithm; the advantage of this method is that it is easy to understand and the algorithm is relatively simple to implement. The biggest problem is that the number of divided clusters needs to be determined in advance, and protein functional modules with overlapping nodes cannot be identified

This type of method has good prediction accuracy and robustness, but its high computational complexity limits its identification in large PPI network functional modules

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