Finding similar patterns in microarray data

Authors:
Xiangsheng Chen;Jiuyong Li;Grant Daggard;Xiaodi Huang
Affiliations:
Department of Mathematics and Computing, The University of Southern Queensland, Australia;Department of Mathematics and Computing, The University of Southern Queensland, Australia;Department of Biological and Physical Sciences, The University of Southern Queensland, Australia;Department of Mathematics, Statistics and Computer Science, The University of New England, Armidale, NSW
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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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.