Data and model driven hybrid approach to activity scoring of cyclic pathways

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buir.contributor.authorAykanat, Cevdet
dc.citation.epage94en_US
dc.citation.spage91en_US
dc.citation.volumeNumber62en_US
dc.contributor.authorIşık, Z.en_US
dc.contributor.authorAtalay V.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.contributor.authorÇetin-Atalay, Rengülen_US
dc.date.accessioned2016-02-08T12:22:56Z
dc.date.available2016-02-08T12:22:56Z
dc.date.issued2010en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: Proceedings of the 25th International Symposium on Computer and Information Sciencesen_US
dc.description.abstractAnalysis of large scale -omics data based on a single tool remains inefficient to reveal molecular basis of cellular events. Therefore, data integration from multiple heterogeneous sources is highly desirable and required. In this study, we developed a data- and model-driven hybrid approach to evaluate biological activity of cellular processes. Biological pathway models were taken as graphs and gene scores were transferred through neighbouring nodes of these graphs. An activity score describes the behaviour of a specific biological process was computed by owing of converged gene scores until reaching a target process. Biological pathway model based approach that we describe in this study is a novel approach in which converged scores are calculated for the cellular processes of a cyclic pathway. The convergence of the activity scores for cyclic graphs were demonstrated on the KEGG pathways. © 2011 Springer Science+Business Media B.V.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:22:56Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2010en
dc.identifier.doi10.1007/978-90-481-9794-1_18en_US
dc.identifier.doi10.1007/978-90-481-9794-1en_US
dc.identifier.urihttp://hdl.handle.net/11693/28524
dc.language.isoEnglishen_US
dc.publisherSpringer, Dordrechten_US
dc.relation.isversionofhttps://doi.org/10.1007/978-90-481-9794-1_18en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-90-481-9794-1en_US
dc.source.titleComputer and Information Sciencesen_US
dc.subjectAS graphen_US
dc.subjectBiological activitiesen_US
dc.subjectBiological pathwaysen_US
dc.subjectBiological processen_US
dc.subjectCellular eventsen_US
dc.subjectCellular processen_US
dc.subjectCyclic graphen_US
dc.subjectData integrationen_US
dc.subjectHeterogeneous sourcesen_US
dc.subjectHybrid approachen_US
dc.subjectModel based approachen_US
dc.subjectModel-drivenen_US
dc.subjectMolecular basisen_US
dc.subjectNeighbouring nodesen_US
dc.subjectGenesen_US
dc.subjectInformation scienceen_US
dc.titleData and model driven hybrid approach to activity scoring of cyclic pathwaysen_US
dc.typeConference Paperen_US

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