Possibilistic approach for biclustering microarray data

Biclustering has emerged as an important method for analyzing gene expression data from microarray technology. It allows to identify groups of genes which behave similarly under a subset of conditions. As a gene may play more than one biological role in conjunction with distinct groups of genes, non-exclusive biclustering algorithms are required. In this paper we propose a new method to obtain potentially-overlapping biclusters, the Possibilistic Spectral Biclustering algorithm (PSB), based on Fuzzy Technology and Spectral Clustering. We tested our method on S. cerevisiae cell cycle expression data and on a human cancer dataset, validating the obtained biclusters using known classifications of conditions and GO Term Finder for functional annotations of genes. Results are available at http://decsai.ugr.es/ approximately ccano/psb.

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