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Table 1 Accuracy of three different machine learning prediction algorithms – J48 Decision Tree, Naïve Bayes and SVM with SMO training – using binary H/P sequences.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% and the bottom 10% of designable conformations for the hexagon 96.8% correct 95.8% correct 98.3% correct
  AUC .97 AUC 0.99 AUC 0.98
  Sens: 1.0 Sens: 1.0 Sens: 0.997
  Spec: 0.94 Spec: 0.92 Spec: 0.97
b) Sequences folding to the top 10% and the bottom 10% of designable conformations for the triangle 92.7% correct 82.4% correct 95.0% correct
  AUC 0.93 AUC 0.92 AUC 0.95
  Sens: 0.93 Sens: 0.76 Sens: 0.92
  Spec: 0.92 Spec: 0.86 Spec: 0.97
  1. a We compare random subsets of sequences corresponding to the top 10% and the bottom 10% of designable structures for the a) hexagon, and b) triangle. Prediction accuracy and area under the curve (AUC), sensitivity (Sens) and specificity (Spec) for each method are given.
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BMC Bioinformatics

ISSN: 1471-2105

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