Sentence examples for using the supervised principal from inspiring English sources

These genes were subsequently ranked based on their absolute Cox regression coefficients, and prognostic models with several sets of top ranking genes using the supervised principal component survival algorithm were developed.

Reasoning that this may be due to different analytical algorithms applied in the previous studies, we attempted to build new signatures using the supervised principal component survival method separately in each of the 4 datasets that comprised the integrated training set.

The signature was derived using the "supervised principal component" [ 16] technique, which in this study leads to a signature involving 86 probe sets.

The signature developed in this study is based on the expression of eight genes and was obtained using the "supervised principal component" technique, similarly to the previous study.

These selected genes were used to develop a gene expression-based prediction model of recurrence risk using the supervised principal component method of E. Bair and R. Tibshirani [ 14].

The signature for predicting survival in AML was developed using the Supervised Principal Components approach which selects genes whose expression patterns are correlated with survival and, based on those, generates a prognostic score (Bair and Tibshirani, 2004).

For the results reported here using the supervised method, we only used unigrams as features.

The survival risk groups were constructed using a predictive index based upon the supervised principal component method of Bair and Tibshirani [ 25].

The supervised principal component technique consists of applying principal component analysis to the set of predictors mostly correlated with the outcome; in this specific case, the authors used the univariate Cox scores as a measure of correlation, and they selected those predictors with absolute Cox score greater than a specific threshold derived by a 10-fold cross-validation procedure.

Mean-centering of the datasets significantly increased the correlation between the supervised principal components and clinical follow up, therefore improving prognostic performance.

The proposed method is semi-supervised since it not only uses the supervised relevance feedback information but also exploits the unlabeled data.