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Table 2 Predictive performance of sequence features for the four benchmark dataset

From: Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features

Features

DT

LR

ANN

Acc.(%)

AUC

Acc.(%)

AUC

Acc.(%)

AUC

746 Dataset

 AAC

83.7

0.897

86.4

0.938

81.0

0.935

 DipC

75.6

0.793

86.4

0.865

91.9

0.974

 PseAAC

78.3

0.787

86.4

0.938

81.0

0.885

 Seq_All

78.3

0.831

86.4

0.847

91.9

0.979 *

1625 Dataset

 AAC

91.4

0.908

84.1

0.904

91.4

0.952

 DipC

92.6

0.861

96.3

0.972

98.7

0.987

 PseAAC

90.2

0.822

87.8

0.921

87.8

0.945

 Seq_All

92.6

0.882

96.3

0.958

98.7

0.984

Schilling Dataset

 AAC

87.7

0.664

86.5

0.856

88.9

0.858

 DipC

87.7

0.526

87.1

0.806

89.5

0.790

 PseAAC

87.1

0.500

86.5

0.864

88.3

0.858

 Seq_All

87.7

0.611

87.7

0.802

87.1

0.821

Impens Dataset

 AAC

85.1

0.500

80.8

0.857

89.3

0.886

 DipC

85.1

0.500

82.9

0.579

93.6

0.893

 PseAAC

87.2

0.721

78.7

0.814

87.2

0.868

 Seq_All

87.2

0.802

85.1

0.696

89.3

0.875

  1. *The best accuracy and AUC in each dataset are underlined