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Surface flatness is very important due to its effect on methods and processes followed for RoI identification.
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Using restart probabilities between 0.1 and 0.9 had little effect on method performance (Additional file 3: Table S3).
Applying the GSC method to interactomes created using different confidence score cutoffs had little effect on method performance (Additional file 3: Table S3).
Experimentation with the values of these parameters was incorporated into the analyses, and was found to have a strong effect on method performance.
The deviations in the estimated predictive accuracy from the simulated true accuracy with and without (benchmark) outliers showed that outliers had the smallest, albeit similar, effects on methods 5 and 7 but were associated with overestimation of accuracy for all methods except 5 and 7 (Table S5, Figure 3, Figure 4, and Figure 5, Figure S7).
The main effects and all interaction order effects on method performance, i.e. namely sensitivity, rapidity and reagent consumption, were determined.
Variations in the matrix composition and their effects on method validation should be given particular attention and be addressed separately [ 191].
The effect of method on the number of bat passes was analyzed using a bootstrapped generalized linear mixed effect model.
By contrast, there was no effect of method on the relative variations ( <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0014788.e231.PNG" class= inline-graphic"/> ).
Naturally, one can see that increasing the sampling rate has a positive effect on all methods.
It is found that these can have a significant effect on the methods considered.
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CEO of Professional Science Editing for Scientists @ prosciediting.com