Research And Implementation Of Dynamic Biological Network Module Analysis Algorithm

Complex networks are of great importance in data mining. The analysis ofbiological networks is an important content of the study of complex networks.Biological systems are dynamic in nature. Through the analysis of the problem of therecognition of dynamic biological network module and the module evolution,researchers can effectively identify functional modules in biological networks topredict protein function prediction of disease factors.In order to find the evolution rules of biological network modules, we introducebased on the description of the basic concepts and statistical properties of the complexnetwork the traditional module analysis algorithm in the field of complex networks.According to the characteristics of dynamic biological networks, we establish adynamic network model and define the related concepts. We study moduleidentification and module evolution problems in dynamic biological networks. We usegreedy algorithm to optimize the modularity function based on module gain. Andanalyze module evolutionary events based on multipartite graph maximum weightmatching algorithm.The experimental results on the life cycle of the Drosophila melanogaster geneexpression network show that our algorithm analyzed the dynamic modules feasibleand effectively. We can explain the life cycle of Drosophila melanogaster at themolecular level. We can identify the functional modules and analyze their growth law.This method can be applied to the analysis of the complex networks used in otherfields. We can identify module evolution according to this method.