Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis

Filippone, M., Masulli, F., Rovetta, S., Mitra, S. and Banka, H. (2006) Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis. Lecture Notes in Computer Science, 4210, pp. 312-322. (doi: 10.1007/11885191_22)

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.

• Item Type: Articles
• Additional Information: The original publication is available at www.springerlink.com
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Filippone, Dr Maurizio
• Authors: Filippone, M., Masulli, F., Rovetta, S., Mitra, S., and Banka, H.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Lecture Notes in Computer Science
• ISSN: 0302-9743
• Copyright Holders: Copyright © 2006 Springer
• First Published: First published in Lecture Notes in Computer Science 4210(2006):312-322
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher