Issue No. 04 - April (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.150
Wen-Hui Yang , Sun Yat-Sen University, Guangzhou
Dao-Qing Dai , Sun Yat-Sen University, Guangzhou
Hong Yan , City University of Hong Kong, Hong Kong
Extracting biologically relevant information from DNA microarrays is a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been proposed for the analysis of gene expression data, but when analyzing the large and heterogeneous collections of gene expression data, conventional clustering algorithms often cannot produce a satisfactory solution. Biclustering algorithm has been presented as an alternative approach to standard clustering techniques to identify local structures from gene expression data set. These patterns may provide clues about the main biological processes associated with different physiological states. In this paper, different from existing bicluster patterns, we first introduce a more general pattern: correlated bicluster, which has intuitive biological interpretation. Then, we propose a novel transform technique based on singular value decomposition so that identifying correlated-bicluster problem from gene expression matrix is transformed into two global clustering problems. The Mixed-Clustering algorithm and the Lift algorithm are devised to efficiently produce \delta-corBiclusters. The biclusters obtained using our method from gene expression data sets of multiple human organs and the yeast Saccharomyces cerevisiae demonstrate clear biological meanings.
Biclustering, pattern classification, gene expression data, singular-value decomposition, data mining, biology computing.
H. Yan, D. Dai and W. Yang, "Finding Correlated Biclusters from Gene Expression Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 568-584, 2010.