The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria

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dc.authorid0000-0003-4469-1440en_US
dc.authorscopusid56115347400en_US
dc.contributor.authorCingiz, Mustafa Özgür
dc.contributor.authorBiricik, Göksel
dc.contributor.authorDiri, Banu
dc.date.accessioned2022-04-21T06:03:38Z
dc.date.available2022-04-21T06:03:38Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractGene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene–gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA–target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs.en_US
dc.identifier.doi10.1007/s12539-021-00440-9en_US
dc.identifier.endpage510en_US
dc.identifier.issn19132751
dc.identifier.issue3en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage500en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1918
dc.identifier.volume13en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorCingiz, Mustafa Özgür
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofInterdisciplinary Sciences: Computational Life Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiological assessment of GCNsen_US
dc.subjectGene co-expression network inference algorithmsen_US
dc.subjectGene co-expression networksen_US
dc.subjectLiterature data-based overlap analysisen_US
dc.subjectTopological analysis of GCNsen_US
dc.titleThe Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteriaen_US
dc.typeArticleen_US

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