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Biclusters Evaluation Based on Shifting and Scaling Patterns

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Intelligent Data Engineering and Automated Learning - IDEAL 2007 (IDEAL 2007)


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.

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Hujun Yin Peter Tino Emilio Corchado Will Byrne Xin Yao

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© 2007 Springer-Verlag Berlin Heidelberg

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Nepomuceno, J.A., Troncoso Lora, A., Aguilar–Ruiz, J.S., García–Gutiérrez, J. (2007). Biclusters Evaluation Based on Shifting and Scaling Patterns. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg.

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  • Print ISBN: 978-3-540-77225-5

  • Online ISBN: 978-3-540-77226-2

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