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Table 3 Performance of Prethermut on the M-dataset with different ranges of absolute ΔΔG

From: Predicting changes in protein thermostability brought about by single- or multi-site mutations

Methoda Range of absolute ΔΔG mb MCC Q2 (%) Sensitivity
(%)
Specificity
(%)
r
RF [0, 1) 1466 0.33 66.8 68.9 65.5 0.39
RF [1, 2) 873 0.57 84.0 78.7 85.2 0.56
RF [2, 3) 509 0.66 91.0 88.1 91.3 0.69
RF [3, 14) 518 0.77 94.8 87.9 95.7 0.72
SVM [0, 1) 1466 0.28 68.3 36.9 87.1 0.31
SVM [1, 2) 873 0.52 86.3 49.7 95.0 0.55
SVM [2, 3) 509 0.64 93.3 57.6 98.0 0.65
SVM [3, 14) 1466 0.62 93.4 44.8 99.6 0.63
  1. All results were obtained by a 10-fold cross validation on the M-dataset. See Methods for definitions of overall accuracy (Q2), Matthews correlation coefficient (MCC), sensitivity, specificity, and Pearson correlation coefficient (r). aThe number of trees in the random forests (RF) method is 10000; the parameters for the support vector machine (SVM) method are gamma (g) = 2, cost (c) = 8, and the weight for the positive samples (w) = 3. bm is the number of mutant proteins in the M-dataset that have the same range of absolute ΔΔG.