Discovering α–Patterns from Gene Expression Data

  • Domingo S. Rodríguez-Baena
  • Norberto Diaz-Diaz
  • Jesús S. Aguilar-Ruiz
  • Isabel Nepomuceno-Chamorro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


The biclustering techniques have the purpose of finding subsets of genes that show similar activity patterns under a subset of conditions. In this paper we characterize a specific type of pattern, that we have called α–pattern, and present an approach that consists in a new biclustering algorithm specifically designed to find α–patterns, in which the gene expression values evolve across the experimental conditions showing a similar behavior inside a band that ranges from 0 up to a pre–defined threshold called α. The α value guarantees the co–expression among genes. We have tested our method on the Yeast dataset and compared the results to the biclustering algorithms of Cheng & Church (2000) and Aguilar & Divina (2005). Results show that the algorithm finds interesting biclusters, grouping genes with similar behaviors and maintaining a very low mean squared residue.


Gene Expression Data Distance Threshold Yeast Dataset Computational Molecular Biology Minimum Experimental Condition 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Domingo S. Rodríguez-Baena
    • 1
  • Norberto Diaz-Diaz
    • 1
  • Jesús S. Aguilar-Ruiz
    • 1
  • Isabel Nepomuceno-Chamorro
    • 2
  1. 1.Pablo de Olavide University, SevilleSpain
  2. 2.Seville University, SevilleSpain

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