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Evaluation of gene selection metrics for tumor cell classification Genet. Mol. Biol.
Faceli,Katti; Carvalho,André C.P.L.F. de; Silva Jr,Wilson A..
Gene expression profiles contain the expression level of thousands of genes. Depending on the issue under investigation, this large amount of data makes analysis impractical. Thus, it is important to select subsets of relevant genes to work with. This paper investigates different metrics for gene selection. The metrics are evaluated based on their ability in selecting genes whose expression profile provides information to distinguish between tumor and normal tissues. This evaluation is made by constructing classifiers using the genes selected by each metric and then comparing the performance of these classifiers. The performance of the classifiers is evaluated using the error rate in the classification of new tissues. As the dataset has few tissue samples,...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Gene selection; Machine learning; Gene expression; Sage.
Ano: 2004 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572004000400029
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