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dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorLaw, Ngai-Fong
dc.contributor.authorYan, Hong
dc.date.accessioned2017-05-03T15:20:04Z
dc.date.available2017-05-03T15:20:04Z
dc.date.issued2011
dc.date.modified2013-05-29T08:13:52Z
dc.identifier.issn1467-5463
dc.identifier.doi10.1093/bib/bbq080
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent251963 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherOxford University Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom498
dc.relation.ispartofpageto513
dc.relation.ispartofissue5
dc.relation.ispartofjournalBriefings in Bioinformatics
dc.relation.ispartofvolume12
dc.rights.retentionY
dc.subject.fieldofresearchBiochemistry and cell biology
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchOther information and computing sciences
dc.subject.fieldofresearchOther information and computing sciences not elsewhere classified
dc.subject.fieldofresearchcode3101
dc.subject.fieldofresearchcode4613
dc.subject.fieldofresearchcode4699
dc.subject.fieldofresearchcode469999
dc.titleMissing value imputation for gene expression data: computational techniques to recover missing data from available information
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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/bbq080
gro.date.issued2011
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


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