It is shown that the performance of an SR reconstruction method on real-world data can be predicted accurately by measuring its performance on simulated data.
To measure the quality of the resulting trees, we measured the topological accuracy on simulated alignments and the likelihood on genuine biological alignments.
The standard practice is to compute a measure of bias based on simulated reads and alignments, similarly to q = s i m u l a t i o n, and remove biased regions from the analysis.
First, three observers measured the optimal RD on simulated fluoroscopy.
This approach is based on a localised basis function neural network, and we have applied it on simulated and measured load states.
The tests were carried out on simulated and measured data and it was found that all-pole models are most suitable than all-zero models when both of the above two aspects must be satisfied by the approximation model.
For comparison of different similarity measures, an objective measurement method is developed in this paper on simulated data.
Water repellency and hydraulic properties were measured on laboratory simulated, and field contaminated soils, 1 and 5 years after an accidental petroleum hydrocarbon spill.
As pointed out earlier, the results obtained on simulated data differ significantly from those measured on empirical data, and for instance, PRANK outperforms all alternative packages on phylogenetic simulated data, but performs poorly when it comes to reconstructing structural alignments.
The approach is tested on simulated ALDs, as well as on an experimentally measured ALD obtained from carbon fiber particles.
