Finding Similar Patterns in Microarray Data

Ziangsheng Chen, Jiuyong Li, Grant Daggard, Xiaodi Huang

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAl 2005
Subtitle of host publicationadvances in artificial intelligence. 18th Australian Joint Conference on Artificial Intelligence
Place of PublicationBerlin, Germany
Number of pages5
Publication statusPublished - 2005
EventAustralian Joint Conference on Artificial Intelligence - Sydney, Australia, Australia
Duration: 05 Dec 200509 Dec 2005


ConferenceAustralian Joint Conference on Artificial Intelligence


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