J Biomed Inform - Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data.

Tópicos

{ model(2341) predict(2261) use(1141) }
{ gene(2352) biolog(1181) express(1162) }
{ featur(3375) classif(2383) classifi(1994) }
{ error(1145) method(1030) estim(1020) }
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{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Our main interest in supervised classification of gene expression data is to infer whether the expressions can discriminate biological characteristics of samples. With thousands of gene expressions to consider, a gene selection has been advocated to decrease classification by including only the discriminating genes. We propose to make the gene selection based on partial least squares and logistic regression random-effects (RE) estimates before the selected genes are evaluated in classification models. We compare the selection with that based on the two-sample t-statistics, a current practice, and modified t-statistics. The results indicate that gene selection based on logistic regression RE estimates is recommended in a general situation, while the selection based on the PLS estimates is recommended when the number of samples is low. Gene selection based on the modified t-statistics performs well when the genes exhibit moderate-to-high variability with moderate group separation. Respecting the characteristics of the data is a key aspect to consider in gene selection.

Resumo Limpo

main interest supervis classif gene express data infer whether express can discrimin biolog characterist sampl thousand gene express consid gene select advoc decreas classif includ discrimin gene propos make gene select base partial least squar logist regress randomeffect re estim select gene evalu classif model compar select base twosampl tstatist current practic modifi tstatist result indic gene select base logist regress re estim recommend general situat select base pls estim recommend number sampl low gene select base modifi tstatist perform well gene exhibit moderatetohigh variabl moder group separ respect characterist data key aspect consid gene select

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