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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.
This process created proxy datasets for testing promoter occupancy, allowing us to look for documented binding of the TFs in sites overlapping the 4,102 promoters, in the relevant cellular models.
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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.
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.
Later, the analyses were reversed with the ARMA dataset being used for training and the VM datasets used for testing.
As in Catanzaro et al. [1], we used the standard Brown and Harrower's datasets [30] for testing the performances of our model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com