Pathway analysis of high-throughput biological data within a Bayesian network framework

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dc.WoS.categoriesBiochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probabilityen_US
dc.contributor.authorOtu, Hasan Hüseyin
dc.contributor.authorJones, Jon
dc.contributor.authorÖztürk, Cengizhan
dc.contributor.authorİşçi, Şenol
dc.date.accessioned2021-01-18T10:32:51Z
dc.date.available2021-01-18T10:32:51Z
dc.date.issued2011-06-15
dc.description.abstractMotivation: Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and nonlinear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Results: Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.1093/bioinformatics/btr269en_US
dc.identifier.issn1460-2059
dc.identifier.issn1367-4803
dc.identifier.pmid21551144en_US
dc.identifier.scopus2-s2.0-79958155496en_US
dc.identifier.urihttps://hdl.handle.net/11411/3124
dc.identifier.urihttps://doi.org/10.1093/bioinformatics/btr269
dc.identifier.wosWOS:000291261300011en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.issue12en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors4en_US
dc.pages1667-1674en_US
dc.publisherOxford Univ Pressen_US
dc.relation.ispartofBioinformaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGENE-EXPRESSION DATAen_US
dc.subjectRENAL-CELL CARCINOMAen_US
dc.subjectFUNCTIONAL-GROUPSen_US
dc.subjectSET ANALYSISen_US
dc.subjectGLOBAL TESTen_US
dc.subjectKNOWLEDGEen_US
dc.subjectGENOMEen_US
dc.subjectMODELen_US
dc.titlePathway analysis of high-throughput biological data within a Bayesian network frameworken_US
dc.typeArticleen_US
dc.volume27en_US

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