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Of the five biochemical features (H, K, M, P and Co), the hydrophobicity index (H) gave the best predictive performance at 74.70% prediction strength (71.62% sensitivity and 77.79% specificity), MCC = 0.4728 and AUC = 0.8237 (Table 2).
In addition, the weight-scoring scheme has its best predictive performance at the (TPR-FPR) of 0.59 (see Additional file 2: Table S2).
Based on the data from Additional file 2: Table S1, quality-score [ 20], PSIPred [ 21, 22], SEG [ 23] and GlobPlot [ 24] obtained their best predictive performance at (TPR-FPR) of 0.61, 0.50, 0.41 and 0.39 respectively.
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As shown in Table 3, BindN-RF gives the best predictive performance with the prediction strength at 76.86% and ROC AUC equal to 0.8495.
Based on the Table, quality-score [ 20], PSIPred [ 21, 22], SEG [ 23] and GlobPlot [ 24] obtained their best predictive performance of (TPR-FPR) at 0.61, 0.50, 0.41 and 0.39 respectively (see entries marked '*' in the last column).
All models were shown to achieve outstanding predictive performance, the lowest being NS2 model with 96.57% accuracy (AUC = 0.980; MCC = 0.916), while HA prediction model achieved the best predictive performance of 98.62% accuracy (AUC = 0.998; MCC = 0.972).
Italic values denote the best predictive performance among the techniques for each data set.
All data were used for developing the models as this gave the best predictive performance, despite the lower positional accuracy of the historical plots.
At the score threshold of 0.5, the weighted-score gives one of the best predictive performance.
As always, multiparametric testing strategies achieve the best predictive performance.
Of the rules we investigated, offers marginally best predictive performance.
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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