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It is evident that our method shows almost the same error as Real-time BP.
It is evident that our method finds a nearly viable path for the motion of the center of mass, as we set out to do.
It is evident that our method outperforms those of [11] and [12], especially for lower QF2 values: when QF2 > QF1, all methods have high AUC values of nearly 0.99, whereas when QF2 < QF1, our method has a better performance.
It is evident that our method has the potential to generate additional biological knowledge from ChIP-seq experiments.
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When we consider both precision and recall, it was evident that our filtering method outperforms the DEG-only method in terms of both F-measure and accuracy.
It is evident that our proposed method delivers competitive performance in comparison with other methods.
> It is evident that our proposed methods, linear kernel machine estimation method (LKME) and Gaussian kernel machine estimation method (GKME), yield competitive performance in all examples.
It is evident that our new method improves the alignment accuracy on highly distorted subtomograms.
Comparing these results with the ones obtained from survival analysis, it seems evident that our feature selection method tends to select probesets more able to predict the class group of test cases, rather than detecting differential survival between Gb groups.
From result and comparison, it is evident that our proposed algorithm performs better in regression based host overload detection method.
In the analyses of MICC data, it was evident that, after applying our method with 3-5 variables, we can achieve up to 75% classification accuracy after applying our methods (Fig. 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