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Under quite realistic experimental conditions, the new estimators outperform classical prediction error methods also when the latter are equipped with an oracle for model order selection.
In particular, it is worth noting that FunPat outperforms the other methods also when data suffers from missing replicates.
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We show results for each of the five methods, and also when combining (using union) predictions of HuMiTar with each of the competing method, as well as for the union of the four competing methods.
Level set-based methods also run faster when a narrow band approach is employed in the segmentation process.
And with the improvement of the reliability, the difference of delay under ERL and URL methods also gradually narrowed, when the reliability greater than 90%%, the delays are almost equal, by this time, the proposed method have a great improvement on reliability and lifetime performance.
The normal distributional method is reliable also when there are small deviations from normality.
Moreover, note that the method also works when the values of the states fluctuate, for example according to a recurring pattern (a limit cycle).
The method also works when we have a user-specified weight function to account for a different emphasis in the misclassification error rates and different priors.
In this paper, we use the standard energy method to deal with the case α = 1 2 ; of course, our method also works when α > 1 2. We will prove the following.
Moreover, note that the method also works when there is no equilibrium but the values of the states fluctuate all the time, according to a recurring pattern (a limit cycle).
We showed that the method also works when the number of genes is larger than the number of perturbations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com