Improving the computational efficiency of recursive cluster elimination for gene selection.
IEEE/ACM Trans Comput Biol Bioinform
; 8(1): 122-9, 2011.
Article
em En
| MEDLINE
| ID: mdl-20479497
The gene expression data are usually provided with a large number of genes and a relatively small number of samples, which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE). However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time cost of SVM-RCE, meanwhile obtains comparable classification performance as SVM-RCE, while LDA-RFE is not stable.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise por Conglomerados
/
Biologia Computacional
/
Análise de Sequência com Séries de Oligonucleotídeos
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Perfilação da Expressão Gênica
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Bases de Dados Genéticas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
/
Male
Idioma:
En
Ano de publicação:
2011
Tipo de documento:
Article