Skip to main content

Table 6 Comparison of prediction performance for KD4i with the DDIG-in and SIFT-Indel methods

From: A comprehensive study of small non-frameshift insertions/deletions in proteins and prediction of their phenotypic effects by a machine learning method (KD4i)

Algorithm Accuracy Sensitivity Specificity Precision NPV MCC
DDIG-in 83 - - - - 0.67
SIFT-Indel 82 81 82 82 - 0.63
KD4i (SVM) 84 80 87 88 79 0.67
KD4i (ILP) (average) 79 89 69 75 85 0.59
KD4i (ILP) (final) 83 93 74 78 91 0.68
  1. For the KD4i (ILP) implementation, the average performances in the 10-fold cross-validation and the performance of the final selected rule set (corresponding to fold 1 in TableĀ 5) are provided. For DDIG-in and SIFT-Indel, the performances for combined insertions and deletions (as originally reported by the authors, for similar balanced data sets) are shown.