Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Shifting and scaling patterns from gene expression data
Bioinformatics
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Evolutionary metaheuristic for biclustering based on linear correlations among genes
Proceedings of the 2010 ACM Symposium on Applied Computing
Comparative analysis of biclustering algorithms
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Correlation–based scatter search for discovering biclusters from gene expression data
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Microarray techniques have motivated the develop of different methods to extract useful information from a biological point of view. Biclustering algorithms obtain a set of genes with the same behaviour over a group of experimental conditions from gene expression data. In order to evaluate the quality of a bicluster, it is useful to identify specific tendencies represented by patterns on data. These patterns describe the behaviour of a bicluster obtained previously by an adequate biclustering technique from gene expression data. In this paper a new measure for evaluating biclusters is proposed. This measure captures a special kind of patterns with scaling trends which represents quality patterns. They are not contemplated with the previous evaluating measure accepted in the literature. This work is a first step to investigate methods that search biclusters based on the concept of shift and scale invariance. Experimental results based on the yeast cell cycle and the human B-cell lymphoma datasets are reported. Finally, the performance of the proposed technique is compared with an optimization method based on the Nelder-Mead Simplex search algorithm.