Identifying Essential Proteins Using Betweenness Centrality & Memgraph MAGE

Proteins are crucial for various biological functions, with essential proteins playing a particularly vital role due to their potential link to diseases, making their identification significant for diagnosis and drug design. Traditional methods for identifying essential proteins are often costly and time-consuming, prompting the use of computational approaches like betweenness centrality. This method assesses the importance of protein nodes within protein-protein interaction networks (PPINs) by measuring the extent to which a node lies on paths between others, helping identify hub proteins that interact extensively with others and are likely essential. Memgraph MAGE, an open-source library, facilitates the calculation of betweenness centrality using graph algorithms, such as those inspired by Brandes' algorithm, to efficiently identify these essential proteins. By visualizing protein networks and analyzing betweenness centrality scores, researchers can pinpoint proteins with significant roles in specific tissues, enhancing understanding and potentially linking them to diseases like Alzheimer's. This innovative computational approach, leveraging the MAGE library's graph algorithms, underscores the importance of network analysis in modern biology for systematic and efficient data analysis, offering additional benefits like persistent data storage and enhanced graph analytics capabilities.