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dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorLaw, Ngai-Fongen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-05-03T15:20:04Z
dc.date.available2017-05-03T15:20:04Z
dc.date.issued2011en_US
dc.date.modified2013-05-29T08:13:52Z
dc.identifier.issn14774054en_US
dc.identifier.doi10.1093/bib/bbq080en_US
dc.identifier.urihttp://hdl.handle.net/10072/37592
dc.description.abstractMicroarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent251963 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherOxford University Pressen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom498en_US
dc.relation.ispartofpageto513en_US
dc.relation.ispartofissue5en_US
dc.relation.ispartofjournalBriefings in Bioinformaticsen_US
dc.relation.ispartofvolume12en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classifieden_US
dc.subject.fieldofresearchcode089999en_US
dc.titleMissing value imputation for gene expression data: computational techniques to recover missing data from available informationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2011 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Briefings in Bioinformatics following peer review. The definitive publisher-authenticated version: Missing value imputation for gene expression data: computational techniques to recover missing datafrom available information, Briefings in Bioinformatics, Vol.12(5), 2011, pp.498-513 is available online at: http://dx.doi.org/10.1093/bib/bbq080en_US
gro.date.issued2011
gro.hasfulltextFull Text


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