Approaches for Network Analysis in Protein Interaction Network

Analysis of protein interaction is important for detailing the cell physiology and predicting disease conditions and drug optimizations. The detection of the crucial proteins in Protein Protein Interaction (PPI) networks is made easier by the accession of these interaction data. The revelation of essential protein nodes in PPI networks is possible using a variety of centrality methods. The hub nodes are decisive in a biological structure because these nodes adjoin profoundly and operate as regulatory hub. The majority of techniques, however, focus on the topological characteristics of PPI. For determining essential proteins, topology and gene annotation are rarely combined. Graph-theoretic methods are used to infer this biological framework in PPI networks. The protein, their interconnections, and the subnetworks are the main subjects of the topological study. In this study, we examine the standard centrality metrics. In order to identify the PPI's prominent nodes and the influence of topological features on centrality metrics, we carefully examined each node's centrality aspect. In this research, we consider Mammalian Protein Database (MIPS) and Biological General Repository for Interaction Networks (BioGRID) datasets and the empirical analysis of individual centrality measures are performed on PPI networks The experimental interpretation shows the behavior of centrality measures on the datasets