Linear coherent bi-cluster discovery via beam detection and sample set clustering

Authors:
Yi Shi;Maryam Hasan;Zhipeng Cai;Guohui Lin;Dale Schuurmans
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
Venue:
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part I
Year:
2010

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Abstract

We propose a new bi-clustering algorithm, LinCoh, for finding linear coherent bi-clusters in gene expression microarray data. Our method exploits a robust technique for identifying conditionally correlated genes, combined with an efficient density based search for clustering sample sets. Experimental results on both synthetic and real datasets demonstrated that LinCoh consistently finds more accurate and higher quality bi-clusters than existing bi-clustering algorithms.