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Our experiments have been conducted on two groups of datasets: synthetic and real world datasets.
The variables selected in this study are reported in Appendix A. Two groups of datasets were used.
Moreover, standard deviations (of 20 times run) for the two groups of datasets are shown in Figures 7 and 8, respectively.
After QC, the two groups of datasets contained 4568 cases and 6255 controls in total, and the numbers of overlapped SNPs were 276,592 in Group A and 477,624 in Group B separately (as shown in Table S1).
To prove that sampling has a significant influence on classification accuracy, we trained two groups of datasets by LibD3C.
We then switched the roles of the two groups of datasets and repeated the same procedure to obtain the normalized likelihoods in all sessions.
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The measurement was repeated four times (two round-trips), and four groups of datasets were collected.
We created four groups of datasets (each group includes 20 human and 20 mouse gene expression matrixes) by randomly removing 5 %, 10 %, 15and 20% and 20%f the origenes datasets, we then identified the mofules using the aboriginalionedatasetsch.
Three groups of datasets include eight vertebrates, twelve fruit flies, and seven yeast species.
In the AUCROC-based clustering, we also observed four clusters of MCMC samplers and four groups of datasets.
All the classification methods were tested by performing 5-fold cross validation on the three groups of datasets.
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