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.
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Filippone, M., Masulli, F., Rovetta, S., Mitra, S., Banka, H. (2006). Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis. In: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2006. Lecture Notes in Computer Science(), vol 4210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11885191_22
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DOI: https://doi.org/10.1007/11885191_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46166-1
Online ISBN: 978-3-540-46167-8
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