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In addition, a good agreement is also obtained for the vertical temperature prediction: an average error of 4% corresponding (average deviation of 0.7 °C).
Compared to the numerical Joule heat prediction an average deviation of 13.1% and 9.0% is determined for the simplified and the enhanced analytical model, respectively.
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A coefficient equals to +1 indicates a perfect prediction; 0, an average random prediction; and (-1), an inverse prediction.
A coefficient of +1 represents a perfect prediction, 0 an average random prediction and −1 an inverse prediction.
A MCC value of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction.
MCC = +1 represents a perfect prediction, 0 an average random prediction and −1 an inverse prediction (Baldi et al., 2000).
Bagging has the advantage of increasing the robustness of the model (reducing the variance of its predictions), by comparison with using a single decision tree, since its prediction is an average of the prediction of many models.
In 80 out of 226 SMP responses tested, the best transcript-based prediction outperformed the best marker-based prediction, with an average improvement in accuracy of 4.8%.
Finally, the best performance is observed by using an ensemble of the predictions from all models, for instance by using a majority prediction or an average vote (improvement 25 and 26%, black bars Fig. 5).
The Z-coordinate prediction has an average error of 1.61 Å. Tobmodel predicts the correct topology for 75% of the proteins in the dataset which is a slight improvement over BOCTOPUS alone.
The results show that the MaxEnt model performs better than random prediction, with an average test area under the curve (AUC) value of 0.93 (0.93–0.93).
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