The results in [6] conclude the ANN prediction model surpasses the LR algorithm in terms of prediction accuracy in the auto-scaling domain.
Moreover, the full, re-fitted TIMI UA/NSTEMI multivariate model surpasses the TIMI UA/NSTEMI risk score in discriminative capacity and improves greatly with the inclusion of heart rate, systolic blood pressure, and either Killip class (for in-hospital mortality) or history of CHF (for 6-month mortality).
Again, the complex model surpasses the simple model over the whole range of p t, the difference between the two models with respect to u overall being clearly visible between c. 0.1 and 0.5.
The variety of the actually existing port governance models surpasses the basic theoretical models.
When both positive and negative instances were considered, the accuracy results showed that: (1) among single feature models, the sequence model outperformed the other two for TFBS recognition; (2) the hybrid model was comparable to the best single feature model (the sequence model) and surpassed the control one.
Side-chain sampling increases the performance significantly to Z = 31.1 and levels out at around 10 resamples per model, surpassing even the state-of-the-art consensus Pcons.
Performance of cross-validation training of the combined model is also shown in Table 4. Surprisingly, prediction accuracy of the final model surpassed all individual protein models, achieving 99.72% accuracy (AUC = 0.999; MCC = 0.994).
Those results suggested, again, that considering both positive and negative instances, performance of the hybrid model was comparable to the best single feature model and surpassed the control one.
The net benefit for both the simple and the complex model surpasses the two extreme strategies in the same range of p t as in Figure 1a.
Secondly, using the modelled-PCM concentrations lead to improved spatial prediction, as a model containing the modelled-PCM concentrations surpassed the model without the modelled-PCM concentrations, with RMSPEs of 0.257 and 0.276 respectively, which is an increase of 7%.
