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In accordance with a prior robustness study (see section "Accessing Robustness") we chose K = 33 prototypes for the analysis in our case study.
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We assessed convergence of the Markov chain Monte Carlo method as well as sensitivity to priors to ensure robustness of estimates to priors (results not shown).
Chromatography software is employed to effectively generate design spaces (Method Operable Design Regions), which are subsequently employed to determine the final method conditions and to evaluate robustness prior to validation.
A large body of prior work improves the robustness of structured networks by augmenting their topologies with redundant routing paths (e.g., [29 31]).
Prior approaches to quantifying robustness rely on the standard deviation of system responses or the expected ratio of performance under perturbation to nominal performance.
Therefore, pre-formation of FP aggregates within the membranes prior to fusion introduce robustness with respect to organelle size.
Additionally, the method performs better than PC even when noisy priors are included in the prior matrix, which shows robustness.
The sensitivity analyses performed in this study, using various prior distributions, support the robustness of the model.
The M U n i f o r m and M D P P models show robustness to prior violations and perform well when applied to data generated under other models.
In these studies, transduced cells expressing high shRNA levels were first enriched by FACS prior to determining the robustness of shRNA-mediated knockdown of target gene expression in such cells.
To assess the effectiveness of prior knowledge incorporation and robustness of kDDN when false positive edges were present in prior knowledge, we examined the network inference precision and recall of the overall network and the differential network at different levels of false positive rate in the prior knowledge.
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