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Table 9 shows the overall average results of each method.
Figure 3 shows typical examples of detection results of each method.
Results of each method are analyzed and presented as follows: 1. Analytical model (by CPLEX) .
After the convergence was achieved the results of each method were compared.
A histogram was created from the 1275 match results of each method, as illustrated in Figure 4.
Estimation difference maps were generated to compare the results of each method, providing more straightforward realizations in a visual way.
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e Result by AL-TFPF. Figure 10 gives the difference between the input data and the de-noised result of each method.
The comparison was performed by means of the segmentation matching factor (SMF) that expressed how precise and reproducible was the vessel and aneurysm segmentation result of each method against the manual segmentation of an experienced radiologist, who was considered as the gold standard.
The integrative approach designed in miRGate outperforms the result of each method separately.
The percentage of these putative outliers in the original result of each method was calculated to measure the method's accuracy.
Those DE-miRNAs detected by at least three methods were considered to be putative PCa associated outliers, and then the percentages of the putative outliers in the original result of each method were calculated to determine the method's accuracy (see Figure 1).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com