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Table 3 Five-fold cross-validation results of the models trained with various features for classifying between 101 carbonylated and 504 non-carbonylated arginine residues

From: Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features

Classifier Training features Sensitivity Specificity Accuracy MCC
SVM AA 0.614 0.603 0.605 0.163
AAC 0.653 0.683 0.678 0.259
AAPC 0.663 0.687 0.683 0.270
PWM 0.713 0.718 0.717 0.336
PSSM 0.624 0.685 0.674 0.239
ASA 0.594 0.599 0.598 0.145
AAindex 0.693 0.726 0.721 0.329
J48 DT AA 0.554 0.603 0.595 0.119
AAC 0.594 0.683 0.668 0.214
AAPC 0.614 0.687 0.674 0.233
PWM 0.614 0.675 0.664 0.222
PSSM 0.554 0.665 0.646 0.169
ASA 0.535 0.599 0.588 0.101
AAindex 0.646 0.690 0.683 0.259
RF AA 0.614 0.605 0.607 0.165
AAC 0.634 0.683 0.674 0.244
AAPC 0.653 0.683 0.678 0.259
PWM 0.713 0.716 0.716 0.334
PSSM 0.624 0.685 0.674 0.239
ASA 0.594 0.599 0.598 0.145
AAindex 0.693 0.724 0.719 0.327
  1. The numbers marked with italicized font are the highest values in four measurements