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Because we do not know the ground truth on real data, we computed the Pearson correlation and Spearman correlation between the results produced by RNA-Skim and one other method, referred to as Pearson (methods) and Spearman (methods) to distinguish from the previous computed correlations between RNA-Skim result and the ground truth.
Since it is difficult to obtain the ground truth for algorithm performance evaluation on real data, we generated two sets of synthetic data and used them to validate the results of our algorithm.
Current practice has been to validate an approach on in-silico synthetic data sets, and, wherever possible, on real data sets with known ground-truth.
The formula is developed based on real data obtained from instrumented buildings that underwent ground motions during earthquakes such as the San Fernando and Northridge earthquakes.
In comparison to speed, memory and the number of mapped reads, alignment accuracy is much harder to evaluate on real data as we do not know the ground truth.
Evaluation on real data is complicated by the lack of a ground truth.
However, it remains difficult to compare different artifact reduction algorithms on real data as the 'ground truth' artifact-free signals are unknown.
The GAM is based on real data.
Section 7 reports experiments on real data.
This is confirmed on real data.
However, the application of the more rigorous Kolmogorov Smirnov test demonstrated that some apparent power-law scaling (as seen from log-log representations) is not based on solid statistical grounds, in real data as well as in the theoretical laws computed, in agreement with previous studies (see e.g. [15]).
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CEO of Professional Science Editing for Scientists @ prosciediting.com