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Table 4 Performance of models based on both positive dataset and negative 2 dataset using SMO as classifier

From: Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

Features

Training

Testing

Sensitivity

Specificity

Accuracy

MCC

Sensitivity

Specificity

Accuracy

MCC

AAC

0.896

0.931

0.914

0.828

0.930

0.932

0.932

0.786

N5C5

0.905

0.931

0.918

0.836

0.960

0.914

0.922

0.772

k-space = 0

0.890

0.929

0.909

0.819

0.940

0.944

0.943

0.819

k-space = 1

0.933

0.950

0.942

0.884

0.910

0.944

0.940

0.803

k-space = 2

0.924

0.942

0.933

0.866

0.910

0.956

0.948

0.825

AAC + k-space = 0

0.892

0.942

0.917

0.835

0.960

0.942

0.945

0.828

AAC + N5C5

0.918

0.920

0.919

0.838

0.960

0.946

0.948

0.836

N5C5 + k-space = 0

0.922

0.948

0.935

0.871

0.970

0.948

0.950

0.843

AAC + N5C5 + k-space 0

0.927

0.950

0.938

0.877

0.970

0.948

0.952

0.847

PSSM

0.950

0.948

0.949

0.898

0.940

0.930

0.932

0.789