Fuzzy logic for elimination of redundant information of microarray data.
10.1016/S1672-0229(08)60021-2
- Author:
Edmundo Bonilla HUERTA
1
;
Béatrice DUVAL
;
Jin-Kao HAO
Author Information
1. LERIA, Université d'Angers, 2 Boulevard Lavoisier, 49045 Angers, France.
- Publication Type:Journal Article
- MeSH:
Colon;
metabolism;
Colonic Neoplasms;
genetics;
Computational Biology;
Data Interpretation, Statistical;
Databases, Genetic;
Fuzzy Logic;
Gene Expression Profiling;
statistics & numerical data;
Humans;
Leukemia;
genetics;
Lymphoma;
genetics;
Models, Statistical;
Oligonucleotide Array Sequence Analysis;
statistics & numerical data
- From:
Genomics, Proteomics & Bioinformatics
2008;6(2):61-73
- CountryChina
- Language:English
-
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.
