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Table 25 Comparison of our results for S1615 data set with previously published studies.

From: Machine learning integration for predicting the effect of single amino acid substitutions on protein stability

Ref. Method Data Set Size Accuracy Information
[41] SVM 2048 0.77 (20-fold cv) Seq
[42] SVM 1383 * 0.73 (20-fold cv) Seq
[9] NN NN+FOLDX 1615 0.79 (20-fold cv) 0.87 (test set†) 0.93 (test set†) Seq+Str
[2] SVM 1496‡ SO: 0.84, TO: 0.85, ST: 0.85 (20-fold cv) SO: 0.86, TO: 0.86, ST: 0.86 (test set) Seq+Str
[31] iPTREE 1615 0.87 (10-fold cv) Seq+Str
Ours Early Late Intermediate 1122 (training) 383 (test) 0.842 (20-fold cv), 0.904 (test set) 0.847 (20-fold cv), 0.903 (test set) 0.826 (20-fold cv), 0.879 (test set) Seq+Str
  1. *Filtered from the set of 2048 mutations [41].
  2. † A subset of the training set that was previously used in training.
  3. ‡ Filtered from the set of 1615 mutations [9].
  4. Machine learning method, data set, performance assessment are the main features to be compared. (Seq: Sequence-based information, Seq+Str: Sequence- and structure-based information)