Finding similar patterns in microarray data

Chen, Xiangsheng and Li, Jiuyong and Daggard, Grant and Huang, Xiaodi (2005) Finding similar patterns in microarray data. In: AI 2005: Advances in artificial intelligence. Lecture Notes in Computer Science (3809). Springer-Verlag, Berlin, Germany, pp. 1272-1276. ISBN 3-540-30462-2

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Abstract

In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.

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Item Type:	Book Chapter (Commonwealth Reporting Category B)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	Author's version deposited with blanket permission of publisher.
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited:	01 Jun 2006
Last Modified:	21 Sep 2016 01:23
Uncontrolled Keywords:	data mining, bioinformatics, microarray data analysis, clustering
Fields of Research (2008):	01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics 06 Biological Sciences > 0604 Genetics > 060405 Gene Expression (incl. Microarray and other genome-wide approaches)
Fields of Research (2020):	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics 31 BIOLOGICAL SCIENCES > 3105 Genetics > 310505 Gene expression (incl. microarray and other genome-wide approaches)
URI:	http://eprints.usq.edu.au/id/eprint/763

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