title: Possibilistic approach for biclustering microarray data.
abstract: 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.
authors.affiliation: Departamento de Ciencias de la Computación e I.A, E.T.S. Ingeniería Informática, University of Granada, Granada, Spain. ccano@decsai.ugr.es
authors.forename: C
authors.initials: C
authors.lastname: Cano
authors.forename: L
authors.initials: L
authors.lastname: Adarve
authors.forename: J
authors.initials: J
authors.lastname: López
authors.forename: A
authors.initials: A
authors.lastname: Blanco
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005800:Genes, Fungal; D006801:Humans; D016393:Lymphoma, B-Cell / Q000235:genetics; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics