Order Preserving Clustering over Multiple Time Course Experiments

  • Stefan Bleuler
  • Eckart Zitzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3449)


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.


Local Search Gene Expression Data Order Preserve Local Search Procedure Multiple Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Stefan Bleuler
    • 1
  • Eckart Zitzler
    • 1
  1. 1.Computer Engineering and Networks Laboratory (TIK)Swiss Federal Institute of Technology ZurichZürichSwitzerland

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