Centrality Measures and Their Applications in Network Analysis: Unveiling Important Elements and Their Impact

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The applications of centrality measures in protein-protein interaction (PPI) network analysis are diverse and encompass fundamental biological insights; cancer disease-related discoveries, and practical implications for drug development. This multidimensional approach in PPI network analysis provides a comprehensive understanding of the pivotal elements and their impact on biological systems. Analyzing centrality measures in PPI networks enables the identification of essential proteins, hub and bottleneck proteins that occupy strategic positions within the PPI network structure. Essential proteins in PPI networks are significant elements that indicate their importance in maintaining PPI network integrity and functionality. Studying centrality measures can reveal hidden patterns and relationships within these PPI networks. This paper identifes PPI networks with a high degree of connectivity (”hubs”) and proteins with high betweenness centrality (bottlenecks), along with closeness centrality and clustering coefficient. This measure’s significance in PPI networks has implications for various felds. The proposed approach successfully identifed and characterized infuential proteins and found the top 20 essential proteins. These proteins likely hold significant functional importance through hubs and bottlenecks and serve as potential targets for further investigation. This approach has the potential to identify essential proteins involved in cancer diseases. Leveraging centrality measures in the analysis of PPI networks ofers a multifaceted approach to understanding cancer biology and its implications for personalized medicine, drug design, and the development of innovative cancer therapies.