Ensemble decision of local similarity indices on the biological network for disease related gene prediction

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dc.authorid0000-0003-4469-1440
dc.contributor.authorCingiz, Mustafa ozgur
dc.date.accessioned2026-02-08T15:16:05Z
dc.date.available2026-02-08T15:16:05Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractLink prediction (LP) is a task for the identification fi cation of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.
dc.identifier.doi10.7717/peerj.17975
dc.identifier.issn2167-8359
dc.identifier.pmid39247551
dc.identifier.scopus2-s2.0-85203464746
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj.17975
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6128
dc.identifier.volume12
dc.identifier.wosWOS:001306799300003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerj Inc
dc.relation.ispartofPeerj
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectLink prediction
dc.subjectBiological network
dc.subjectLocal similarity based indices
dc.subjectEnsemble learning
dc.subjectBioinformatics
dc.subjectGene Ontology analysis
dc.titleEnsemble decision of local similarity indices on the biological network for disease related gene prediction
dc.typeArticle

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