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Accurate and robust gene selection for disease classification using a simple statistic

Discrimination of disease patients based on gene expression data is a crucial problem in clinical area. An important issue to solve this problem is to find a discriminative subset of genes from thousands of genes on a microarray or DNA chip. Aiming at finding informative genes for disease classifica...

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Detalles Bibliográficos
Autores principales: Mutsubayashi, Hikaru, Aso, Seiichiro, Nagashima, Tomomasa, Okada, Yoshifumi
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics Publishing Group 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637954/
https://www.ncbi.nlm.nih.gov/pubmed/19238233
Descripción
Sumario:Discrimination of disease patients based on gene expression data is a crucial problem in clinical area. An important issue to solve this problem is to find a discriminative subset of genes from thousands of genes on a microarray or DNA chip. Aiming at finding informative genes for disease classification on microarray, we present a gene selection method based on the forward variable (gene) selection method (FSM) and show, using typical public microarray datasets, that our method can extract a small set of genes being crucial for discriminating different classes with a very high accuracy almost closed to perfect classification.