title: Fuzzy logic for elimination of redundant information of microarray data.
abstract: Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equivalence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, extensive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.
authors.affiliation: LERIA, Université d'Angers, 2 Boulevard Lavoisier, 49045 Angers, France.
authors.forename: Edmundo Bonilla
authors.initials: EB
authors.lastname: Huerta
authors.forename: Béatrice
authors.initials: B
authors.lastname: Duval
authors.forename: Jin-Kao
authors.initials: JK
authors.lastname: Hao
mesh_terms: D003106:Colon / Q000378:metabolism; D003110:Colonic Neoplasms / Q000235:genetics; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D007938:Leukemia / Q000235:genetics; D008223:Lymphoma / Q000235:genetics; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*