Finding Similar Patterns in Microarray Data

Abstract. In this paper we propose a clustering algorithm called sCluster 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.

• Type: Conference
• Year: 2005
• Where: AUSAI
• Authors: Xiangsheng Chen, Jiuyong Li, Grant Daggard, Xiaodi Huang
• Keywords: Algorithm Called SCluster, Artificial Intelligence, AUSAI 2005, Similar Expression Patterns, Strong Similar Expression