Moreover, the use of height in this study enabled us to demonstrate that the dataset is large enough to differentiate the patterns of BMI and height between generations.
Our studies demonstrated that the datasets obtained from the arrays can be used for typical analyses, including PCA and the analysis of differential expression.
This result demonstrates that the datasets are uniform, within a small threshold, when they contain a sufficient number of points, as would be expected with a random distribution.
The microarray data demonstrated that the dataset can be used for typical analyses, including principal component analysis (PCA) and differential gene expression analysis.
The identification of inner ear-related miRNAs by RNA-seq analysis demonstrates that the dataset is reliable not only for characterizing expression profiles of known miRNAs, but also for discovery of novel miRNAs in the inner ear.
Collectively, the analyses performed here clearly demonstrate that the transcriptome datasets generated for the current study enable the global identification of genotype-specific expression differences.
The results clearly demonstrate that the H37Rv physical interologs dataset is much more similar to the H37Rv pull-down interologs dataset than to the H37Rv B2H PPI dataset.
Experimental results on the dataset demonstrate that the proposed framework can achieve superior detection performance to the state-of-the-art approaches.
The experimental results on the UCI benchmark datasets and the high dimensional cancer gene expression datasets demonstrate that the proposed feature transformation methods are superior or competitive to several classical FT methods.
Experimental results on benchmark datasets demonstrate that the proposed system is robust and efficient.
