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To evaluate the prospective predictive performance of the model, predictions were generated for 13 known inhibitors and inducers of CYP3A4 that were not included in the analysis dataset. Figure 2i shows the observed vs. predicted MDZ AUC ratio for these compounds.

However, for 3liz, 3jr9, 3zkm and 3r1g, which have >300 residues, reasonable epitope predictions were generated, suggesting that the method retains a degree of predictive power even for larger antigens.

The model predictions were generated from the color-object association data shown in Fig. 4.

Production predictions were generated by the network for all locations at which seismic attributes were available.

Predictions were generated for the compounds in the WOMBAT database using a model created solely on activity data from ChEMBL.

Additional predictions were generated, using the William Landel Ferry time-temperature empirical relations, for material performance at temperatures above and below the reference temperature range.

Transcription factor recognition site predictions were generated with MatInspector [66].

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In the case of classification, predictions are generated by averaging probability estimates, no by voting.

Computational predictions are generated using the high temperature kinetic models of Sarathy et al. (2012) and Merchant et al. (2013).

Predictions are generated using a numerical mode matching approach for axially uniform defects, and a hybrid finite element based method for non-uniform defects.

For each of these, a tabular aerodynamic model based on CFD predictions is generated along with validation against wind tunnel experiments and flight test measurements.

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