Finding Additive Biclusters with Random Background
CPM '08 Proceedings of the 19th annual symposium on Combinatorial Pattern Matching
Expert Systems with Applications: An International Journal
Mean Square Residue Biclustering with Missing Data and Row Inversions
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting
COCOA '09 Proceedings of the 3rd International Conference on Combinatorial Optimization and Applications
IEEE Transactions on Fuzzy Systems
An automatic gene ontology software tool for bicluster and cluster comparisons
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
MIB: Using mutual information for biclustering gene expression data
Pattern Recognition
Phoenix: privacy preserving biclustering on horizontally partitioned data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Stability-based validation of bicluster solutions
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
Linear coherent bi-cluster discovery via beam detection and sample set clustering
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part I
WF-MSB: A weighted fuzzy-based biclustering method for gene expression data
International Journal of Data Mining and Bioinformatics
An effective measure for assessing the quality of biclusters
Computers in Biology and Medicine
Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Ensemble methods for biclustering tasks
Pattern Recognition
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
Sparse learning based linear coherent bi-clustering
WABI'12 Proceedings of the 12th international conference on Algorithms in Bioinformatics
Improving project-profit prediction using a two-stage forecasting system
Computers and Industrial Engineering
Tourism demand forecasting using novel hybrid system
Expert Systems with Applications: An International Journal
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Motivations: Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions. Results: We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters. Availability: The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html. Contact: lwang@cs.cityu.edu.hk Supplementary information: The Supplementary Data is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/supp.html.