A Clustering-Based Method for Gene Selection to Classify Tissue Samples in Lung Cancer
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This paper proposes a gene selection approach based on clustering of DNA-microarray data. The proposal has been aimed at finding a boundary gene subset coming from gene groupings imposed by a clustering method applied to the case study: gene expression data in lung cancer. Thus, we assume that such a found gene subset represents informative genes, which can be used to train a classifier by learning tumor tissue samples. To do this, we compare the results of several methods of hierarchical clustering to select the best one and then choose the most suitable clustering based on visualization techniques. The latter is used to compute its boundary genes. The results achieved from the case study have shown the reliability of this approach.
KeywordsDNA-microarray Feature selection Data clustering Genetic algorithm Data mining Visual analytics
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