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dc.contributor.authorZeng, Jiaen_US
dc.contributor.authorZhu, Shanfengen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-04-24T12:16:03Z
dc.date.available2017-04-24T12:16:03Z
dc.date.issued2010en_US
dc.date.modified2011-04-18T06:56:23Z
dc.identifier.issn14712105en_US
dc.identifier.doihttp://www.biomedcentral.com/content/pdf/1471-2105-11-164.pdfen_AU
dc.identifier.urihttp://hdl.handle.net/10072/38204
dc.description.abstractBackground Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods. Conclusions The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent486749 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherBioMed Central Ltd.en_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto13en_US
dc.relation.ispartofjournalBMC Bioinformaticsen_US
dc.relation.ispartofvolume11en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleMulticonstrained gene clustering based on generalized projectionsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2010 Liew et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_AU
gro.date.issued2010
gro.hasfulltextFull Text


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