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Gene Expression Biclustering Using Random Walk Strategies

  • Fabrizio Angiulli
  • Clara Pizzuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3589)

Abstract

A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a gain function, combining the mean squared residue, the row variance, and the size of the bicluster. Different strategies to escape local minima are introduced and compared. Experimental results on yeast and lymphoma microarray data sets show that the method is able to find significant biclusters.

Keywords

Random Move Gain Function Yeast Cell Cycle Local Search Strategy Gene Expression Data Analysis 
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

  • Fabrizio Angiulli
    • 1
  • Clara Pizzuti
    • 1
  1. 1.ICAR-CNRUniversità della CalabriaRendeItaly

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