How to Identify Essential Proteins Using Betweenness Centrality

Identifying essential proteins has become crucial for understanding disease diagnosis and treatment, with computational methods like betweenness centrality increasingly favored over traditional biological experiments due to their efficiency. Utilizing Memgraph, a graph analytics platform, researchers can perform complex analyses on protein-protein interaction networks, with betweenness centrality helping to pinpoint essential proteins, such as the APP protein linked to Alzheimer's disease in Cochlea tissue. This approach, enabled by the Memgraph Advanced Graph Extensions (MAGE) library, leverages graph theory techniques to explore the intricate and highly connected nature of these networks, providing a more accessible, cost-effective alternative to resource-intensive experiments. Moreover, the study showcases the potential of other graph algorithms, such as PageRank, and introduces a web application for visualizing these interactions, offering a practical tool for analyzing protein networks in human tissues.