To discuss the LVZs mentioned above, we compared our results with the distribution of aftershocks of the 2008 earthquake and S-wave reflectors.
For each individual pathway mentioned above, we compared the PES values obtained from the three methods.
For reasons already mentioned, we did not compare the result between in presence and absence of p53.
As previously mentioned, we were unable to compare the rates of technical accuracy in the RUSG-PDT and S-PDT groups because of inconsistency in procedural documentation, including the observed level of placement and insertion in the midline.
As mentioned earlier, we compare the performance of the proposed scheme with the existing schemes; the ONCCP scheme [16] and the OCAEH scheme [8]. Figure 3 shows the performance comparison of each scheme in terms of the normalized system throughput.
As we mentioned before, we compare our method based on decomposition of a multi-class feature selection problem into a set of two-class problems with a well known 'all classes at once' technique.
We compared posts mentioning AEs detected in Twitter with public data derived from the US FDA Adverse Event Reporting System FAERSS).
It is also worth mentioning that if we compare the centroid estimator in Figures 6 and 7, benefits of using the proposed geometrical estimators are as big as the mean scatterer diameter increases.
From the tools mentioned in the introduction, we compare to jActiveModules, KeyPathwayMiner and ExprEssence.
A total of 45 ES were mentioned, and compared among different sectors and levels of governance.
