Identifying Essential Hub Genes and Protein Complexes in Malaria GO Data using Semantic Similarity Measures

TL;DR

This study employs semantic similarity measures and network analysis to identify essential hub genes and protein complexes in malaria-related gene ontology data, aiding understanding of molecular interactions.

Contribution

It introduces a novel approach combining semantic similarity measures with network clustering to identify key proteins and complexes in malaria GO data.

Findings

Identified critical hub genes in malaria GO data.

Built protein-protein interaction networks revealing protein complexes.

Demonstrated effectiveness of semantic similarity measures in biological network analysis.

Abstract

Hub genes play an essential role in biological systems because of their interaction with other genes. A vocabulary used in bioinformatics called Gene Ontology (GO) describes how genes and proteins operate. This flexible ontology illustrates the operation of molecular, biological, and cellular processes (Pmol, Pbio, Pcel). There are various methodologies that can be analyzed to determine semantic similarity. Research in this study, we employ the jack-knife method by taking into account 4 well-liked Semantic similarity measures namely Jaccard similarity, Cosine similarity, Pairsewise document similarity, and Levenshtein distance. Based on these similarity values, the protein-protein interaction network (PPI) of Malaria GO (Gene Ontology) data is built, which causes clusters of identical or related protein complexes (Px) to form. The hub nodes of the network are these necessary proteins. We use a variety of centrality measures to establish clusters of these networks in order to determine which node is the most important. The clusters' unique formation makes it simple to determine which class of Px they are allied to.