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First, a subset of s% of the samples is selected, stratified relative to the outcome variable of interest.
It was found that different genes can be identified as prognosis markers depending on which subset of patient samples is selected as the training dataset [ 16], further casting the doubt on the current methodology of developing prognostic gene signatures from the whole genome transcription profiles.
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Some samples were selected for stability testing.
Two samples were selected for reduction.
These samples were selected based on random sampling method.
Unweathered white marble samples were selected as stone samples.
Abnormally high resistivity and normal resistivity core samples were selected.
Samples were selected to calculate energy trap depth parameters.
At higher-z, our samples are selected in the CDFS.
The optimum sintering temperature samples were selected for characterization.
To conduct the tests, six samples were selected.
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