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While our rescoring method leads to significant improvements of the final success rates of binding site predictions, performance of the classifier itself is less satisfactory (see Table 2).
We chose two criteria to evaluate the performance of our binding-site predictions: performance at less than 5% FPR and the Matthews correlation coefficient (MCC).
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Prediction performance can then be evaluated by comparing predictive and observed cancer status.
Developing novel methods for predicting protein submitochondrial locations is not only a race of prediction performance.
13688_2016_85_MOESM1_ESM.pdf Full results of prediction performance.
Figure 16 Prediction performance for book adoption.
When these criteria were analyzed, ANN showed high prediction performance, while MLR showed low prediction performance.
As expected, a low noise floor increases the prediction performance.
Figure 4 Prediction performance sorted by noise floors.
Overall, SVM classifier showed better prediction performance than SRC and KELM.
Figure 5 Prediction performance sorted by pilot SNR.
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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