Sentence examples for robust cutoff from inspiring English sources

The FDR that is obtained by this method accounts for both the sequencing depth and the background noise, giving a robust cutoff when identifying RDNPs.

A robust cutoff value for CDK1SA was derived by choosing a threshold with maximum log-rank statistics (Hothorn and Zeileis, 2008).

However, we objectively determined robust cutoff values using a nonparametric resampling method that does not require any predefined assumptions or distributional specifications.

Robust cutoff values for these factors could be obtained utilising maximum log-rank statistics for threshold definition (Hothorn and Zeileis, 2008).

We also utilize a more robust cutoff to identify pigment regulators (~50% normalized pigment production) to identify only those genes with significant impacts on pigment production.

To evaluate and eliminate the effect of sequencing depth and background noise, we used a sampling method to empirically estimate the FDR, which provided a robust cutoff for comparing the different pairs (see Section 2).

Although we aimed to choose specific and robust cutoffs for the definition of the utilized outcomes for this meta-analysis, outcome-specific bias cannot be excluded due to the combination of data from different studies in which different definitions for each outcome were used.

On the basis of this curve, we set the robust z-score cutoff value for hit identification to 1.49, which identified 9 of the 12 annotated GCs.

Here, examination of the ROC curve led us to a robust z-score cutoff of 12.3 that identified 11 of the 12 bona fide GCs, with the prodrug prednisone again being the exception.

Furthermore, the rate estimation of insertion/deletion is robust for different cutoffs used for determining gene homologues.

These show that our method is robust with the GI cutoff and p-value cutoff of AfnScore, although its performance gradually decreases with the increase of gene expression cutoffs.