Two Novel Approaches to Detect Community: A Case Study of Omicron Lineage Variants PPI Network
Mamata Das, Selvakumar K., P.J.A. Alphonse

TL;DR
This paper introduces two new algorithms, ABCDE and ALCDE, for detecting communities in the Omicron variant's protein interaction network, comparing them with existing methods to enhance understanding of viral structure.
Contribution
The study proposes two novel community detection algorithms and evaluates their performance against established methods on the Omicron PPI network.
Findings
The new algorithms effectively identify meaningful communities within the Omicron network.
Comparison shows the proposed methods perform competitively with existing algorithms.
Insights into the network's modular structure can aid in understanding viral mechanisms.
Abstract
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…
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Taxonomy
TopicsBioinformatics and Genomic Networks · Advanced Biosensing Techniques and Applications · Machine Learning in Bioinformatics
