Sentence examples for restrictive cutoff from inspiring English sources

Consequently, this approach only needs to choose a numeric value between 0 and 100%%, being those close to 0%% very restrictive cutoff points (delivering high precision) and those close to 100%% (delivering high recall).

For example, even at the restrictive cutoff used in our analysis there are 345 differentially expressed genes in the metabolism GO category compared to 181 in their data.

At a restrictive cutoff of q<0.01, 1,897 of 8,347 detected genes (22.7%) differed between CR and HIGHCAL (3,855 genes (46.2%) at the permissive cutoff of q<0.1).

The number of terms returned for each of the upregulated (U), downregulated (D), and combined upregulated and downregulated (C) sets of genes are as follows: at the restrictive cutoff q<0.01 U = 57, D = 61, and C = 103; at the permissive cutoff q<0.1 U = 94, D = 99, and C = 170.

Choosing a very restrictive cutoff score for additional sequences (e.g. 12.5) reduced the total number of sequences available, and gave lower robustness in the jack-knife test.

Villumsen et al. established a national, very restrictive cutoff in order to obtain a high specificity and a high predictive value of a positive result [ 21]; this decision was based on the assumption that Q fever was sporadic in Denmark.

We separately analyzed upregulated, downregulated, and the combined upregulated and downregulated sets of genes using both permissive and restrictive cutoffs for a total of 6 separate analyses.

Figure 4 displays the frequencies (at both permissive and restrictive cutoffs) of each of four classes of genes: increased by CR and more highly expressed (UP) in females, increased by CR and UP in males, decreased by CR and displaying lower expression (DOWN) in females, and decreased by CR and DOWN in males.

These experiments underscored the classic tradeoff between sensitivity (or recall) and precision: restrictive cutoffs yield the fewest false positives, improving precision, but reducing the recall rate.

For any given comparison and using the same restrictive cutoffs mentioned above, we found that the overall methylation variability referred to all samples was attributable, in decreasing order, to (i) the different cell types as expected, (ii) the condition health/disease only in iPSC‐derived DAn, and (iii) inter‐individual differences in a relative lesser extent.

At a more restrictive distance cutoff of RMSD≤0.1 Å, the performance difference becomes more pronounced, with 88.0% of H-atoms added by HAAD falling in this category, while only 76.6% and 59.9% of H-atoms predicted by REDUCE and HBUILD are in this category, respectively.