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The predictor has an estimated false positive rate of 11% and false negative rate of 74% if a protein is predicted as a substrate with a posterior probability of at least 0.9 (Figure S1).
Taking together the estimates in the internal (LOOCV) and external validation, the present predictor has an estimated 94.4% sensitivity and 85.7% specificity for the prediction of infliximab response which is the highest performance reached by a biomarker for this therapy to date.
Non-significant for mitochondrial rates, this predictor has an r 2 of around 50% for both classes of nuclear site (Table 1).
A classifier with better performance than a random predictor has an AUC between 0.5 and 1. DLD-1 (ATCC CCL-221) cells were obtained from the American Type Culture Collection and maintained in High Glucose Dulbecco Modified Essential Media supplemented with 10% Fetal bovine sera and 2 mM L-Glutamine.
It can be seen that the prediction accuracies for each group were 100%, 69.23%, 55.56%, and 90.32%, respectively, while the overall prediction accuracy was 87.50%, which is even better than that of the training dataset, indicating that the predictor has an excellent generalization.
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The proposed predictor has a cascade structure in such a way that the first system in the cascade allows to estimate the delayed state while each of the remaining ones is an appropriate predictor.
Finally, the statistical significance determines whether a predictor has a significant explanatory value.
For a cut-off of 27.156 the predictor has a sensitivity of 94.1% and a specificity of 61.1%.
Here, the video quality predictor has a local error assessment unit, besides having statistics from motion vectors.
Similarly to the CH predictor, CDF predictor has a parameter (CDF count), the mean of which over all proteins sequences for a gene is compared to the threshold to determine a single prediction for the gene.
Recently it has been indicated that the α-MoRF predictor has a poor sensitivity, i.e., misses many α-MoRF regions [ 37], due to the small set of α-MoRF regions used in its development.
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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