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With the aim of evaluating the performance of some of the main contributions of our proposal, we use the publicly available CAVIAR dataset for testing the proposed tracking method with a success near to 85% in occlusion situations.
Hence, we believe, this was a suitable dataset for testing the hypothesis described in the Background section – that is, the multi-label multi-class approaches are more apt than the ubiquitous single-label multi-class schemes when it comes to the application of ligand-based classifiers to target-fishing.
This is repeated for 4 times utilizing each time a different fourth of dataset for testing.
The 15 OTUs provide a secure dataset for testing the contribution of SW Asian early Neolithic farmers to the southeastern and central European gene pool.
For all three tissues, for both FOS and JUN, we had relatively large numbers of peaks in each file to compare with the 4,102 promoters of interest, providing an excellent dataset for testing the hypotheses that these TFs occupy the promoters preferentially in MET versus non-cancer cells and in MET versus Non-MET cancer, but not in Non-MET versus non-cancer cells.
Remaining dataset for testing performance (second dataset).
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All relevant experimental literature is critically assessed to identify datasets for testing our model.
Here, we additionally generated and analyzed in-vitro simulated datasets for testing the performances of BayesAss+.
Moreover, we prepared two datasets for testing the performance of the best model in the study.
This shows the importance of using multiple datasets for testing to get a fair comparison between methods; for example, WS had a better average ROC-50 score than PWM-based BBLS due to the good score on NRSF, but overall, BBLS PWM is slightly better than WS (p-value 0.064).
Next, we compiled mixed datasets for testing the read-mapping pipeline, by randomly sampling subsets of these simulated sequence reads.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com