Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Discovering significant OPSM subspace clusters in massive gene expression data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Positive and Negative Co-regulation Patterns from Microarray Data
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
BicAT: a biclustering analysis toolbox
Bioinformatics
Random walk biclustering for microarray data
Information Sciences: an International Journal
Biclustering gene expression data using KMeans-binary PSO hybrid
ISB '10 Proceedings of the International Symposium on Biocomputing
MIB: Using mutual information for biclustering gene expression data
Pattern Recognition
A novel approach for biclustering gene expression data using modular singular value decomposition
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Information Sciences: an International Journal
Information Sciences: an International Journal
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
Analysing microarray expression data through effective clustering
Information Sciences: an International Journal
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Biclusters are subsets of genes that exhibit similar behavior over a set of conditions. A biclustering algorithm is a useful tool for uncovering groups of genes involved in the same cellular processes and groups of conditions under which these processes take place. In this paper, we propose a polynomial time algorithm to identify functionally highly correlated biclusters. Our algorithm identifies (1) gene sets that simultaneously exhibit additive, multiplicative, and combined patterns and allow high levels of noise, (2) multiple, possibly overlapped, and diverse gene sets, (3) biclusters that simultaneously exhibit negatively and positively correlated gene sets, and (4) gene sets for which the functional association is very high. We validate the level of functional association in our method by using the GO database, protein-protein interactions and KEGG pathways.