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Generated data set was tested with MLR method to control its achievement.
As for the BYEC classifier, 70% of the original data set were used to train the KNN, BPNN, and SVM classifier and 30% were used to evaluate the accuracy on the original data set, then the accuracy of these classifiers on the processed data set was tested by 90% of the processed data set.
Each data set was tested for normality using a Shapiro-Wilk's test.
Prior to statistical analyses, each data set was tested for normal distribution and homogeneity of variances.
To determine whether the BY-kinases were subjected to selection, codon alignment from the BYKsel data set was tested.
The whole data set was tested for contamination by applying blastn against the entire GenBank database with default values.
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Therefore the number of required eigenmodes for each data set is tested.
Finally, the accuracy of the adjusted BYEC classifier and the original classifier on the processed data set were tested by the remaining 90% of the processed data.
The significant SNPs for HTDMYslope and THIslope from the discovery data set were tested in this validation data set.
However, both the linearity ratios data set and numbers of trials required to achieve 100 successful trials data set were tested with the non-parametric Kruskal-Wallis test.
Conversely, from a statistical viewpoint, when a data set is tested in multiple angles, the threshold for statistical significance should be adjusted to reduce the inflated type I error.
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