For ease and efficiency of generating a dataset to demonstrate the concept used in the interface, this information was extracted and created manually from the existing archives.
This DFEs-based approach offers faster calculation even in the case when there is only a single dataset to demonstrate the principles.
This study includes a "leave-one-class-out" experiment on the pendigits dataset to demonstrate the detection ability of the proposed OOR method for hidden classes.
Also, we used this dataset to demonstrate that some popular functions designed for representing the high-viscosity segment (where the activation energy changes with temperature) are not recommendable for approximating the low-viscosity segment (where the activation energy is constant).
We also evaluated the performance of tests by analyzing a real sequence dataset to demonstrate the utility of the proposed tests.
We use the Xu et al. dataset to demonstrate the DE analysis process of BitSeq.
We used HuRef (VenterInst, 2014), a publicly available dataset, to demonstrate the effectiveness of FastHap for individual haplotype reconstruction.
We compared the leaf RNA sequencing dataset with the present leaf dataset to demonstrate and confirm that our microarray analysis approach towards transcriptome profiling was appropriate.
