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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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, X., Li, J., Daggard, G., Huang, X. (2005). Finding Similar Patterns in Microarray Data. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_185
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DOI: https://doi.org/10.1007/11589990_185
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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