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

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