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
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary biclustering of microarray data
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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The biclustering techniques have the purpose of finding subsets of genes that show similar activity patterns under a subset of conditions. In this paper we characterize a specific type of pattern, that we have called α-pattern, and present an approach that consists in a new biclustering algorithm specifically designed to find α-patterns, in which the gene expression values evolve across the experimental conditions showing a similar behavior inside a band that ranges from 0 up to a pre-defined threshold called a. The a value guarantees the co-expression among genes. We have tested our method on the Yeast dataset and compared the results to the biclustering algorithms of Cheng & Church (2000) and Aguilar & Divina (2005). Results show that the algorithm finds interesting biclusters, grouping genes with similar behaviors and maintaining a very low mean squared residue.