The Research Of Analyzing Community Detection In Complex Biomedical Networks Based On Betweenness And Modularity

In recent years, the research of complex networks has attracted widespread concern from researchers which from biomedical, sociology, physics, and many other different fields. It become an important challenge in scientific research work for analysis approach which use quantitative, qualitative, and the network topological structure of complex networks to help researchers found the general valuable rule hidden in complex networks. As the most important structure characteristics which present recognized, the structure and function analysis of Community structure has important theoretical significance and practical value for the complex network. As the abstract pattern of biological system, complex biomedical network has play an important pole in the study of biological mechanism(function). The study of complex biomedical network can explore hidden rule and information from the network.It has important theoretical significance and practical significance for clinical medicine, drug treatment and medical research.This paper mainly studies the application for classic algorithm for example the GN(M. Girvan- m. e. j. Newman) algorithm, FN(Fast- Newman) algorithm, improved weighted GN algorithm.Using the above algorithm to analysis the complex biomedical network which includes the protein-protein interaction data from HPRD(Human Protein Reference Database) Human protein-protein interacting database, CORUM mammals protein complex database, Reactome Human biology reaction and signaling pathway database, and Traditional Chinese medicine(TCM) text mining related data.Finally making a contrast analysis on analysis for the experimental results with Biological Process、Cellular Component、Diseases Gene、 Molecular Function 、 Tissue Expressions five modules, then come to the conclusion.We can get the conclusion that experimental result in the HPRD database has obvious differentiation in genetic diseases modules analysis.It hint that FN algorithm has potential applications value in biomedical research. It has important theoretical significance and practical value for us to discovering important information or the general rules hidden potential in biomedical network using the data mining algorithm reasonable on gene/protein and disease research.