Parallel distributed kernel estimation
Computational Statistics & Data Analysis
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Quick Hierarchical Biclustering on Microarray Gene Expression Data
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
MIB: Using mutual information for biclustering gene expression data
Pattern Recognition
Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
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Biclustering is an important tool to find patterns in a microarray data matrix by simultaneous classification in two dimensions of genes and conditions. Unlike most existed biclustering algorithms where almost all genes and conditions are involved in the clustering process even if they contribute little to a bicluster, we propose to perform the biclustering operation only in related genes and conditions of a given bicluster type. In our algorithm, the gene expression matrix is first partitioned to stable and unstable submatrices in both row and column directions by inspecting the similarity between the row (or column) vector and the full 1s vector, then the related genes and conditions of a given type of biclusters are extracted by inspecting the row or column pairs in the corresponding stable or unstable submatrices, finally the resulted biclusters of any type are obtained by performing clustering analysis in the extracted related genes and conditions. Additionally, a novel strategy for estimating the missing data in the gene expression matrix is also presented based on the James-Stein and kernel estimation principle where the estimation matrix is obtained with the k means algorithm. Experimental results show excellent performance of our algorithm both in missing data estimation and biclustering.