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
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Analyzing time series gene expression data
Bioinformatics
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
A multi-objective approach to discover biclusters in microarray data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
An evolutionary approach for biclustering of gene expression data
International Journal of Bio-Inspired Computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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Clustering still represents the most commonly used technique to analyze gene expression data—be it classical clustering approaches that aim at finding biologically relevant gene groups or biclustering methods that focus on identifying subset of genes that behave similarly over a subset of conditions. Usually, the measurements of different experiments are mixed together in a single gene expression matrix, where the information about which experiments belong together, e.g., in the context of a time course, is lost. This paper investigates the question of how to exploit the information about related experiments and to effectively use it in the clustering process. To this end, the idea of order preserving clusters that has been presented in [2] is extended and integrated in an evolutionary algorithm framework that allows simultaneous clustering over multiple time course experiments while keeping the distinct time series data separate.