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Article: A Weighted Local Least Squares Imputation method for missing value estimation in microarray gene expression data

TitleA Weighted Local Least Squares Imputation method for missing value estimation in microarray gene expression data
Authors
KeywordsGene expression data analysis
Missing value imputation
Vector angle
Weighted Local Least Square Imputation
WLLSI
Issue Date2010
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmb
Citation
International Journal Of Data Mining And Bioinformatics, 2010, v. 4 n. 3, p. 331-347 How to Cite?
AbstractMany clustering techniques and classification methods for analysing microarray data require a complete dataset. However, very often gene expression datasets contain missing values due to various reasons. In this paper, we first propose to use vector angle as a measurement for the similarity between genes. We then propose the Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. Numerical results on both synthetic data and real microarray data indicate that WLLSI method is more robust. The imputation methods are then applied to a breast cancer dataset and interesting results are obtained. Copyright© 2010 Inderscience Enterprises Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/75469
ISSN
2015 Impact Factor: 0.528
2015 SCImago Journal Rankings: 0.289
ISI Accession Number ID
Funding AgencyGrant Number
RGC7017/07P
HKU10206647
10206483
10206147
Funding Information:

The authors would like to thank the three anonymous referees for their helpful comments and suggestions in the revision of the paper and Dr. Shigeyuki Oba for providing datasets. Wai-Ki Ching is supported in part by RGC Grant 7017/07P, HKU Strategic Research Theme Fund on Computational Physics and Numerical Methods, Hung Hing Ying Physical Research Fund, HKU GRCC Grants Nos. 10206647, 10206483 and 10206147.

References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorLi, Len_HK
dc.contributor.authorTsing, NKen_HK
dc.contributor.authorTai, CWen_HK
dc.contributor.authorNg, TWen_HK
dc.contributor.authorWong, ASen_HK
dc.contributor.authorCheng, KWen_HK
dc.date.accessioned2010-09-06T07:11:24Z-
dc.date.available2010-09-06T07:11:24Z-
dc.date.issued2010en_HK
dc.identifier.citationInternational Journal Of Data Mining And Bioinformatics, 2010, v. 4 n. 3, p. 331-347en_HK
dc.identifier.issn1748-5673en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75469-
dc.description.abstractMany clustering techniques and classification methods for analysing microarray data require a complete dataset. However, very often gene expression datasets contain missing values due to various reasons. In this paper, we first propose to use vector angle as a measurement for the similarity between genes. We then propose the Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. Numerical results on both synthetic data and real microarray data indicate that WLLSI method is more robust. The imputation methods are then applied to a breast cancer dataset and interesting results are obtained. Copyright© 2010 Inderscience Enterprises Ltd.en_HK
dc.languageengen_HK
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmben_HK
dc.relation.ispartofInternational Journal of Data Mining and Bioinformaticsen_HK
dc.subjectGene expression data analysisen_HK
dc.subjectMissing value imputationen_HK
dc.subjectVector angleen_HK
dc.subjectWeighted Local Least Square Imputationen_HK
dc.subjectWLLSIen_HK
dc.subject.meshGene Expressionen_HK
dc.subject.meshGene Expression Profiling - methodsen_HK
dc.subject.meshLeast-Squares Analysisen_HK
dc.subject.meshOligonucleotide Array Sequence Analysis - methodsen_HK
dc.titleA Weighted Local Least Squares Imputation method for missing value estimation in microarray gene expression dataen_HK
dc.typeArticleen_HK
dc.identifier.emailChing, WK: wching@hku.hken_HK
dc.identifier.emailTsing, NK: nktsing@hku.hken_HK
dc.identifier.emailNg, TW: ngtw@hku.hken_HK
dc.identifier.emailWong, AS: awong1@hkucc.hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.identifier.authorityTsing, NK=rp00794en_HK
dc.identifier.authorityNg, TW=rp00768en_HK
dc.identifier.authorityWong, AS=rp00805en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJDMB.2010.033524en_HK
dc.identifier.pmid20681483-
dc.identifier.scopuseid_2-s2.0-77953156609en_HK
dc.identifier.hkuros170054en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77953156609&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.issue3en_HK
dc.identifier.spage331en_HK
dc.identifier.epage347en_HK
dc.identifier.isiWOS:000280011300006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridLi, L=35329863000en_HK
dc.identifier.scopusauthoridTsing, NK=6602663351en_HK
dc.identifier.scopusauthoridTai, CW=36099428000en_HK
dc.identifier.scopusauthoridNg, TW=7402229732en_HK
dc.identifier.scopusauthoridWong, AS=23987963300en_HK
dc.identifier.scopusauthoridCheng, KW=35081802000en_HK

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