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
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Finding Time-Lagged 3D Clusters
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Mining time-shifting co-regulation patterns from gene expression data
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Efficiently mining time-delayed gene expression patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Chilean seismic regionalization through a Kohonen neural network
Neural Computing and Applications
Algorithm for Discovering Low-Variance 3-Clusters from Real-Valued Datasets
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
The ParTriCluster algorithm for gene expression analysis
International Journal of Parallel Programming
Pattern recognition in biological time series
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
An evolutionary algorithm to discover quantitative association rules in multidimensional time series
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)
IEEE Transactions on Evolutionary Computation
Fusion of knowledge towards the identification of genetic profiles
AI Communications
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Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology.