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Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping.
The bioinformatics integration of protein networks with large gene expression datasets was demonstrated, over a decade ago, to be highly useful in the elucidation of signaling functions [ 7].
The general application of the maximum likelihood (MLHD) classifier implemented in a genetic algorithm to microarray datasets was demonstrated by Trevino and Ooi [ 25, 26].
The quality of the microarray datasets was demonstrated by verifying reproducibility among replicates by hierarchical cluster analysis using Pearson correlation and average linkage.
The particular ability of SWIFT to detect rare populations in large datasets was demonstrated by comparing with three other programs, FLAME, flowMerge, and flowMeans 7, 20, 21, using graded sample sizes, and testing for the identification of rare cytokine-producing populations (see Supporting Information for details).
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The results on the MemeTracker and Sina Weibo datasets are demonstrated in Figs. 3 and 4, respectively.
The effect of filtering low-intensity spots from the datasets is demonstrated in supplementary Figure S4 [ 21].
The consistency of the prognostic value across datasets is demonstrated by the forest plots in Figures 2c and 2d, where the results of the analysis of individual datasets are concisely summarized by the 5-year survival estimates and hazard ratios between the 'good' and 'poor' groups.
The sensor stability was also evaluated over the course of over 100 days and the ability to retrain ANNs with a small dataset was demonstrated.
Because the feasibility of GLCM texture analysis by using the OAI dataset was demonstrated by Carballido-Gamio and colleagues [ 53], their study provided the foundation for the present study.
Superior performance from the enhanced G2G formulation incorporating the soil dataset is demonstrated.
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