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Table 3 Accuracy of machine learning predictions classifying sequences folding to the most designable conformations among random binary sequences for a) hexagonal and b) triangular shapes.a

From: Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

  J48 Naïve Bayes SMO
a) Sequences folding to the top 10% of designable structures vs. random binary sequences of length 19 for the hexagon 97.2% correct 94.2% correct 97.3% correct
  AUC 0.97 AUC 0.98 AUC 0.98
  Sens: 1.0 Sens: 1.0 Sens: 0.997
  Spec: 0.94 Spec: 0.89 Spec: 0.95
b) Sequences folding to the top 10% of designable structures vs. random binary sequences of length 21 for the triangle 90.3% correct 84.4% correct 95.2% correct
  AUC 0.91 AUC 0.92 AUC 0.95
  Sens: 0.93 Sens: 0.92 Sens: 0.97
  Spec: 0.90 Spec: 0.82 Spec: 0.94
  1. a Prediction accuracy and area under the curve (AUC), sensitivity (Sens) and specificity (Spec) for each method are given.