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Conference Paper: Clustering of SNP data with application to genomics

TitleClustering of SNP data with application to genomics
Authors
Ng, MKLi, MJAo, SISham, PCCheung, YMHuang, JZ
Issue Date2006
Citation
The 6th IEEE International Conference on Data Mining - Workshops (ICDM 2006), Hong Kong, China, 18 December 2006. In Conference Proceedings, 2006, p. 158-162 How to Cite?
DOI: http://dx.doi.org/10.1109/ICDMW.2006.43
AbstractSingle nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. Many methods may only be applicable when marker haplotypes, rather than genotypes (categorical data), are available for analysis. In this paper, we explore the properties of k-modes (categorical data) clustering algorithms to SNP data for detecting association between SNP markers and disease. Subspace k-modes clustering properties are also considered and tested. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/176091
ISBN
978-076952702-4
ISSN
1550-4786
2020 SCImago Journal Rankings: 0.545
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorNg, MKen_US
dc.contributor.authorLi, MJen_US
dc.contributor.authorAo, SIen_US
dc.contributor.authorSham, PCen_US
dc.contributor.authorCheung, YMen_US
dc.contributor.authorHuang, JZen_US
dc.date.accessioned2012-11-26T09:05:34Z-
dc.date.available2012-11-26T09:05:34Z-
dc.date.issued2006en_US
dc.identifier.citationThe 6th IEEE International Conference on Data Mining - Workshops (ICDM 2006), Hong Kong, China, 18 December 2006. In Conference Proceedings, 2006, p. 158-162en_US
dc.identifier.isbn978-076952702-4-
dc.identifier.issn1550-4786en_US
dc.identifier.urihttp://hdl.handle.net/10722/176091-
dc.description.abstractSingle nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. Many methods may only be applicable when marker haplotypes, rather than genotypes (categorical data), are available for analysis. In this paper, we explore the properties of k-modes (categorical data) clustering algorithms to SNP data for detecting association between SNP markers and disease. Subspace k-modes clustering properties are also considered and tested. © 2006 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofIEEE International Conference on Data Mining, ICDM 2006 Proceedingsen_US
dc.titleClustering of SNP data with application to genomicsen_US
dc.typeConference_Paperen_US
dc.identifier.emailSham, PC: pcsham@hku.hken_US
dc.identifier.authoritySham, PC=rp00459en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ICDMW.2006.43-
dc.identifier.scopuseid_2-s2.0-38049055687en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-38049055687&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage158en_US
dc.identifier.epage162en_US
dc.identifier.scopusauthoridNg, MK=34571761900en_US
dc.identifier.scopusauthoridLi, MJ=16024793200en_US
dc.identifier.scopusauthoridAo, SI=8581155100en_US
dc.identifier.scopusauthoridSham, PC=34573429300en_US
dc.identifier.scopusauthoridCheung, YM=24465110000en_US
dc.identifier.scopusauthoridHuang, JZ=36107803800en_US
dc.customcontrol.immutablesml 170602 amended-
dc.identifier.issnl1550-4786-

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