Order Preserving Clustering over Multiple Time Course Experiments

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Order Preserving Clustering over Multiple Time Course Experiments
Abstract. 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. 1 Motivation A central goal in the analys...
Stefan Bleuler, Eckart Zitzler
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where EVOW
Authors Stefan Bleuler, Eckart Zitzler
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