Method for predicting function module and function on basis of PPI network hierarchy structure

Abstract

The invention relates to a method for predicting a function module and function on the basis of the PPI network hierarchy structure. According to the technical scheme, the method comprises the steps that PPI network and biological information are input, a hierarchy tree T is built according to a protein-protein interaction network, the hierarchy tree T is coded according to likelihood value calculation of the protein-protein interaction network, a genetic algorithm of a maximum likelihood value hierarchy tree T is sought, and function module mining and function prediction are conducted. Accordingly, the defects that in the sparse PPI network low in density, the effect is poor, and randomness exists are overcome. According to the method, mining and function prediction are conducted on the function module according to the maximum likelihood value hierarchy tree T, and function module mining and function prediction are conducted simultaneously through network likelihood value calculation;on the basis of considering network topology, corresponding biological information is blended, an internal relationship among network nodes is reflected, many unnecessary density calculations are reduced, the prediction result is more accurate, and the reliability of the prediction result is improved.

Description

technical field [0001] The invention belongs to the technical field of biological information, and mainly relates to a technology for mining functional modules and function predictions through a network hierarchical structure analysis algorithm in a protein interaction network, and particularly relates to a method for predicting functional modules and functions based on network hierarchical structures in a PPI network. Background technique [0002] Protein interaction network (PPI) plays an important role in life activities, and has important application value in the aspects of organism survival, drug target design, disease treatment and prediction. Although some achievements have been made in the mining of functional modules in protein interaction networks, due to the high complexity and randomness of living systems, effective methods in other fields often do not necessarily achieve ideal results in PPI network analysis, resulting in The predicted protein accuracy is low. ...

AI Technical Summary

Problems solved by technology

The disadvantages of mining functional modules and role prediction in this way are: (1) The existing methods can effectively detect functional modules with high density, but the effect is not good in sparse PPI networks with low density

(2) Due to the high complexity and randomness of living systems, the method of mining functional modules by calculating network density often does not necessarily achieve ideal results

Also because the interaction and interconnection of proteins in the PPI network are random, it is more difficult to obtain the optimal solution

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