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Article dans une revue

Fuzzy Logic for Elimination of Redundant Information of Microarray Data

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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.

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https://hal.univ-angers.fr/hal-03255368
Contributeur : Okina Université d'Angers <>
Soumis le : mercredi 9 juin 2021 - 14:58:15
Dernière modification le : jeudi 10 juin 2021 - 03:39:57

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Edmundo Bonilla Huerta, Béatrice Duval, Jin-Kao Hao. Fuzzy Logic for Elimination of Redundant Information of Microarray Data. Genomics, Proteomics and Bioinformatics, Elsevier, 2008, 6 (2), pp.61 - 73. ⟨10.1016/S1672-0229(08)60021-2⟩. ⟨hal-03255368⟩

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