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Computational Methods in Systems Biology

Volume 4210 of the series Lecture Notes in Computer Science pp 312-322

Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis

  • Maurizio FilipponeAffiliated withDISI, Dept. Computer and Information Sciences, University of Genova and CNISM
  • , Francesco MasulliAffiliated withDISI, Dept. Computer and Information Sciences, University of Genova and CNISM
  • , Stefano RovettaAffiliated withDISI, Dept. Computer and Information Sciences, University of Genova and CNISM
  • , Sushmita MitraAffiliated withCenter for Soft Computing: A National Facility, Indian Statistical InstituteMachine Intelligence Unit, Indian Statistical Institute
  • , Haider BankaAffiliated withCenter for Soft Computing: A National Facility, Indian Statistical Institute

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Abstract

The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. In this paper we introduce a new approach to the biclustering problem using the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. The biclustering problem, in which one would maximize the size of the bicluster and minimizing the residual, is faced as the optimization of a proper functional. We applied the algorithm to the Yeast database, obtaining fast convergence and good quality solutions. We discuss the effects of parameter tuning and the sensitivity of the method to parameter values. Comparisons with other methods from the literature are also presented.