Abstract
Biclustering is powerful data mining technique that allows identifying groups of genes which are co-regulated and co-expressed under a subset of conditions for analyzing gene expression data from microarray technology. Possibilistic biclustering algorithms can give much insight towards different biological processes that each gene might participate into and the conditions under which its participation is most effective. This paper proposes an iterative algorithm that is able to produce k-possibly overlapping semi-possibilistic (or soft) biclusters satisfying input constraints. Several previous possibilistic approaches are sensitive to their input parameters and initial conditions beside that they don’t allow constraints to be put on the residue of produced biclusters and can work only as refinement step after applying hard biclustering. Our semi-possibilistic approach allows discovering overlapping biclusters with meaningful memberships while reducing the effect of very small memberships that may participate in iterations of possibilistic approaches. Experimental study on Yeast and Human shows that our algorithm can offer substantial improvements in terms of the quality of the output biclusters over several previously proposed biclustering algorithms.
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Mahfouz, M.A., Ismail, M.A. (2012). Soft Flexible Overlapping Biclustering Utilizing Hybrid Search Strategies. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_32
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DOI: https://doi.org/10.1007/978-3-642-35326-0_32
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