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A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
BACKGROUND: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently...
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| 主要な著者: | , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
BioMed Central
2006
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC1569875/ https://ncbi.nlm.nih.gov/pubmed/16670007 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-7-235 |
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