Finding k-biclusters from gene expression data

Authors:
Xiaohua Xu;Ping He;Lin Lu;Yanqiu Xi;Zhoujin Pan
Affiliations:
Department of Computer Science, Yangzhou University, Yangzhou, China;Department of Computer Science, Yangzhou University, Yangzhou, China;Department of Computer Science, Yangzhou University, Yangzhou, China;Department of Computer Science, Yangzhou University, Yangzhou, China;Department of Computer Science, Yangzhou University, Yangzhou, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

This paper addresses searching in the gene expression data for more meaningful blocks, namely the introduced "steady" biclusters. To this end, we propose a k-means algorithm which aims at finding k biclusters from gene expression data. Experimental results demonstrate that the algorithm can efficiently find the co-regulation patterns, especially those highly homogenous with little difference from each other.