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dc.contributor.authorÇarkacioǧlu, L.en_US
dc.contributor.authorAtalay, R.en_US
dc.contributor.authorKonu, O.en_US
dc.contributor.authorAtalay, V.en_US
dc.contributor.authorCan, T.en_US
dc.date.accessioned2016-02-08T09:55:31Z
dc.date.available2016-02-08T09:55:31Z
dc.date.issued2010en_US
dc.identifier.issn1748-5673
dc.identifier.urihttp://hdl.handle.net/11693/22099
dc.description.abstractDue to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations. Copyright © 2010 Inderscience Enterprises Ltd.en_US
dc.language.isoEnglishen_US
dc.source.titleInternational Journal of Data Mining and Bioinformaticsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1504/IJDMB.2010.037548en_US
dc.subjectAPDen_US
dc.subjectAssociation pattern discoveryen_US
dc.subjectBiclusteringen_US
dc.subjectGene expression analysisen_US
dc.subjectSpearman rank correlationen_US
dc.titleBi-k-bi clustering: mining large scale gene expression data using two-level biclusteringen_US
dc.typeArticleen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.citation.spage701en_US
dc.citation.epage721en_US
dc.citation.volumeNumber4en_US
dc.citation.issueNumber6en_US
dc.identifier.doi10.1504/IJDMB.2010.037548en_US
dc.publisherInderscience Enterprises Ltd.en_US


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