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Then the two datasets were selected to detect site-specific positive selection and purifying selection.
Calibration and validation datasets were selected randomly.
These datasets were selected based on completeness, accuracy, and consistent data formatting.
Three sample datasets were selected using the pre-processing technique described in the previous section.
These datasets were selected for their properties, mainly due to their distinct class distributions (Table 1).
Large screening datasets were selected from PubChem [19] to represent a spread in terms of size and ratio of active to inactive compounds.
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More specifically, 1/6 of the human datasets was selected as independent test datasets and the remaining human datasets selected as training datasets.
For illustration purpose, two publicly available credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed weighted LSSVM classifier.
Two popular face datasets are selected to evaluate the experimental discretization performance in this section: This employed dataset is a subset of the FERET face dataset [29], in which the images were collected under varying illumination conditions and face expressions.
This pool, including probe sets that were part of significant prognostic models in all datasets, was selected for the custom array.
One of the datasets was selected to illustrate the flow of a typical analysis.
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CEO of Professional Science Editing for Scientists @ prosciediting.com