The phrase "active true" is not a standard expression in written English and may cause confusion.
It could potentially be used in a specific context related to programming or logic, where "active" and "true" are relevant terms.
Example: "In the system settings, ensure that the option for 'active true' is selected to enable the feature."
Alternatives: "enabled and valid" or "active and confirmed".
So, inactive true knees adjacent active true knees are simplicial vertices in G3 that can be deleted forming G4.
Let G1 be the instance G I plus these additional edges and consider the following sets: S1={False shoulders, inactive false knees }; S2={Head}; S3={Inactive true knee adjacent to an active true knee }; S4={Active false knee adjacent to an inactive false knee }; S5={Active false knee adjacent to an inactive true knee (in the same clause component) }, and.
A 2x2 confusion matrix consists of four sections: True positives (TP) for active compounds correctly classified as active, false positives (FP) for inactive compounds incorrectly classified as active, true negatives (TN) for inactive compounds correctly classified as inactive and false negatives (FN) for active compounds incorrectly classified as inactive.
The four sections of a confusion matrix are True Positives (TP) - in our case Active compounds correctly classified as Active; False Positives (FP) - Inactive compounds incorrectly classified as Active; True Negatives (TN) - Inactive compounds correctly classified as Inactive; False Negatives (FN) - Active compounds incorrectly classified as Inactive.
The receiver operator characteristics (ROC) curve plots the fraction of molecules correctly predicted as active (true positive rate or sensitivity) against the fraction of inactive molecules that are falsely predicted as active (false positive rate or 1‐specificity) for every possible decision threshold.
Percent actives is the ratio of actives to the total number of actives (true postives) in the dataset given by equation 3. % Actives = TP A × 100 (3).
The percentages of actives (true positives) and decoys (false positives) that remain in the docked database after each of the pharmacophore filters.
Figure 3 Bar chart illustrating filtering rates for actives vs. decoys with neuraminidase A. The percentages of actives (true positives) and decoys (false positives) that remain in the docked database after each of the pharmacophore filters.
The ROC curve plots the fraction of correctly predicted actives (true positive rate) against the fraction of inactives incorrectly predicted as actives (false positive rate) for every possible threshold.
The Receiver Operating Characteristic (ROC) curve displaying the fraction of ranked actives (true positive rate) at a given fraction of ranked decoys (false positive rate) was plotted for each VS run.
After this initial models evaluation, all compounds from a particular group of molecules (actives, true inactives, DUDs, and ZINC) were docked into the constructed homology models and crystal structures.
