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Comparisons are made at a large number (∼77,000) of discrete time intervals, allowing error estimates to be given as a function of cutoff rigidity.
We then assessed the quality of our predictions against the strong and medium signals in DRSC RNAi screening results as the positive reference set, and the non-signal results as the negative reference set, with the performance of our model assessed as precision vs. recall and precision as a function of cutoff score (Figure 4).
Results in Figure 4 show network size for the empiric NF and CFS classes as a function of cutoff p-value for edge weight significance.
We subsequently define the enrichment plot, which illustrates the absolute value of the logarithm of the HG score as a function of cutoff percentage.
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Receiver operator characteristic curves constructed to graphically represent the relationship between sensitivity and specificity as a function of the cutoff value for normal latency.
To choose an appropriate PCC cutoff value to construct co-expression network, we examined the changes in the node number, edge number, and network density as a function of PCC cutoff values.
The second-degree polynomial function, as a function of elevation cutoff angle, was fitted in weighted least-squares to the coordinate differences; this is given as a solid line in Fig. 6.
Consequently, if the carrier photogeneration occurs in the whole source spectrum, the plot of the photocurrent as a function of the cutoff wavelengths is expected to be a monotone curve which is increasing when moving towards shorter wavelengths.
For each of the three types of interactions centers, the predictive performance for the training set in terms of the Pearson Correlation Coefficient (PCC) was reported as a function of the cutoff distance used in the scoring function.
Ref. [15] also provides a study of the "GO correlation" (fraction of pairs of coexpressed genes sharing their most specific GO annotation) as a function of the cutoff in Pearson correlation coefficient used to define coexpression.
Figure 2 shows the number of enriched modules and GO classes as a function of the cutoff p-value for enrichments.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com