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Table 5 Five-fold cross-validation results of the models trained with various features for classifying between 94 carbonylated and 412 non-carbonylated proline 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

0.638

0.655

0.652

0.233

AAC

0.713

0.716

0.715

0.347

AAPC

0.646

0.728

0.713

0.309

PWM

0.745

0.733

0.735

0.388

PSSM

0.670

0.709

0.702

0.307

ASA

0.585

0.607

0.603

0.151

AAindex

0.702

0.752

0.743

0.375

J48 DT

AA

0.617

0.607

0.609

0.176

AAC

0.638

0.631

0.632

0.212

AAPC

0.638

0.636

0.636

0.216

PWM

0.660

0.680

0.676

0.271

PSSM

0.670

0.709

0.702

0.307

ASA

0.574

0.583

0.581

0.123

AAindex

0.649

0.709

0.698

0.290

RF

AA

0.628

0.660

0.654

0.229

AAC

0.723

0.716

0.717

0.355

AAPC

0.646

0.728

0.713

0.309

PWM

0.734

0.733

0.733

0.380

PSSM

0.660

0.704

0.696

0.294

ASA

0.585

0.607

0.603

0.151

AAindex

0.734

0.743

0.741

0.390

  1. The numbers makred with italicized font are the highest values in four measurements