Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Clustering Algorithms
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
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
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
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Finding biclusters by random projections
Theoretical Computer Science
A novel approach to revealing positive and negative co-regulated genes
Journal of Computer Science and Technology
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
On mining micro-array data by Order-Preserving Submatrix
International Journal of Bioinformatics Research and Applications
Gene interaction - An evolutionary biclustering approach
Information Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Discovering α-patterns from gene expression data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Order preserving clustering by finding frequent orders in gene expression data
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
International Journal of Data Mining and Bioinformatics
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Mining biologically significant co-regulation patterns from microarray data
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Algorithmic and complexity issues of three clustering methods in microarray data analysis
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
A linear time biclustering algorithm for time series gene expression data
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Order preserving clustering over multiple time course experiments
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Local correlation detection with linearity enhancement in streaming data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
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The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. Our work focuses on discovering a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. Specifically, we are looking for the Order Preserving clusters (OPCluster), in each of which a subset of genes induce a similar linear ordering along a subset of conditions. The pioneering work of the OPSM model[3], which enforces the strict order shared by the genes in a cluster, is included in our model as a special case. Our model is more robust than OPSM because similarly expressed conditions are allowed to form order equivalent groups and no restriction is placed on the order within a group. Guided by our model, we design and implement a deterministic algorithm, namely OPCTree, to discover OP-Clusters. Experimental study on two real datasets demonstrates the effectiveness of the algorithm in the application of tissue classification and cell cycle identification. In addition, a large percentage of OP-Clusters exhibit significant enrichment of one or more function categories, which implies that OP-Clusters indeed carry significant biological relevance.