Two Novel Approaches to Detect Community: A Case Study of Omicron Lineage Variants PPI Network
This work addresses the need for better understanding disease mechanisms in virology, but it is incremental as it applies new algorithms to a specific dataset without broad SOTA claims.
The study tackled the problem of identifying communities in the Omicron virus protein-protein interaction network using two novel algorithms (ABCDE and ALCDE) and compared them with four established methods, resulting in insights into structural organization validated by modularity metrics.
The capacity to identify and analyze protein-protein interactions, along with their internal modular organization, plays a crucial role in comprehending the intricate mechanisms underlying biological processes at the molecular level. We can learn a lot about the structure and dynamics of these interactions by using network analysis. We can improve our understanding of the biological roots of disease pathogenesis by recognizing network communities. This knowledge, in turn, holds significant potential for driving advancements in drug discovery and facilitating personalized medicine approaches for disease treatment. In this study, we aimed to uncover the communities within the variant B.1.1.529 (Omicron virus) using two proposed novel algorithm (ABCDE and ALCDE) and four widely recognized algorithms: Girvan-Newman, Louvain, Leiden, and Label Propagation algorithm. Each of these algorithms has established prominence in the field and offers unique perspectives on identifying communities within complex networks. We also compare the networks by the global properties, statistic summary, subgraph count, graphlet and validate by the modulaity. By employing these approaches, we sought to gain deeper insights into the structural organization and interconnections present within the Omicron virus network.