Biclustering of expression microarray data using affinity propagation

  • Authors:
  • Alessandro Farinelli;Matteo Denitto;Manuele Bicego

  • Affiliations:
  • University of Verona, Department of Computer Science, Verona, Italy;University of Verona, Department of Computer Science, Verona, Italy;University of Verona, Department of Computer Science, Verona, Italy

  • Venue:
  • PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
  • Year:
  • 2011

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Abstract

Biclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis. In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering. Specifically, we cast Affinity Propagation into the Couple Two Way Clustering scheme, which allows to use a clustering technique to perform biclustering. We extend the CTWC approach, adapting it to Affinity Propagation, by introducing a stability criterion and by devising an approach to automatically assemble couples of stable clusters into biclusters. Empirical results, obtained in a synthetic benchmark for biclustering, show that our approach is extremely competitive with respect to the state of the art, achieving an accuracy of 91% in the worst case performance and 100% accuracy for all tested noise levels in the best case.