Clustering of SNP data with application to genomics

Kwok Po NG; Mark J. Li; Sio I. Ao; Pak C. Sham; Yiu Ming CHEUNG; Joshua Z. Huang

doi:10.1109/icdmw.2006.43

Clustering of SNP data with application to genomics

Kwok Po NG, Mark J. Li, Sio I. Ao, Pak C. Sham, Yiu Ming CHEUNG, Joshua Z. Huang

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

7 Citations (Scopus)

Abstract

Single 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.

Original language	English
Title of host publication	Proceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
Publisher	IEEE
Pages	158-162
Number of pages	5
ISBN (Print)	0769527027, 9780769527024
DOIs	https://doi.org/10.1109/icdmw.2006.43
Publication status	Published - 2006

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Scopus Subject Areas

Engineering(all)

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10.1109/icdmw.2006.43

Cite this

NG, K. P., Li, M. J., Ao, S. I., Sham, P. C., CHEUNG, Y. M., & Huang, J. Z. (2006). Clustering of SNP data with application to genomics. In Proceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops (pp. 158-162). Article 4063617 (Proceedings - IEEE International Conference on Data Mining, ICDM). IEEE. https://doi.org/10.1109/icdmw.2006.43

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abstract = "Single 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.",

author = "NG, {Kwok Po} and Li, {Mark J.} and Ao, {Sio I.} and Sham, {Pak C.} and CHEUNG, {Yiu Ming} and Huang, {Joshua Z.}",

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isbn = "0769527027",

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Clustering of SNP data with application to genomics. / NG, Kwok Po; Li, Mark J.; Ao, Sio I. et al.
Proceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops. IEEE, 2006. p. 158-162 4063617 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Sham, Pak C.

AU - CHEUNG, Yiu Ming

AU - Huang, Joshua Z.

PY - 2006

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AB - Single 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.

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Clustering of SNP data with application to genomics

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