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dc.contributor.authorHuang, Qinghuaen_US
dc.contributor.authorTao, Dachengen_US
dc.contributor.authorLi, Xuelongen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.date.accessioned2017-04-24T12:52:08Z
dc.date.available2017-04-24T12:52:08Z
dc.date.issued2012en_US
dc.date.modified2013-06-03T04:55:51Z
dc.identifier.issn15455963en_US
dc.identifier.doi10.1109/TCBB.2011.53en_US
dc.identifier.urihttp://hdl.handle.net/10072/47637
dc.description.abstractThe analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherIEEE/ACMen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom560en_US
dc.relation.ispartofpageto570en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalIEEE - ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.relation.ispartofvolume9en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchNeural, Evolutionary and Fuzzy Computationen_US
dc.subject.fieldofresearchcode080109en_US
dc.subject.fieldofresearchcode080108en_US
dc.titleParallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2012
gro.hasfulltextNo Full Text


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