Skip to main content

Table 2 The performance of various SVM models was developed using AC, DC, PSSM profiles and Hybrid methods on the individual Pg-activators SAK, SK, UK and tPA in five-fold cross validation

From: Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators

Proteins Methods ACC(%) SN(%) SP(%) MCC Parameters
       γ C
SAK AC 96.06 92.30 96.91 0.93 3 300
DC 86.82 87.08 86.76 0.83 3 75
PSSM 93.98 92.28 94.34 0.92 1 300
Hybrid 91.72 96.63 90.62 0.93 1 150
SK AC 95.77 99.05 92.92 0.95 3 275
DC 86.40 93.12 80.56 0.82 10 25
PSSM 97.10 100 94.44 0.97 1 400
Hybrid 90.75 99.05 83.55 0.90 1 250
tPA AC 95.83 100 95.71 0.97 50 100
DC 92.70 70.58 93.35 0.78 15 450
PSSM 97.73 100 97.67 0.98 4 200
Hybrid 92.69 75.00 93.20 0.81 10 450
UK AC 90.68 100 86.77 0.93 3 300
DC 87.03 95.03 83.66 0.87 15 500
PSSM 92.06 93.19 91.56 0.90 5 9
Hybrid 85.03 99.40 79.00 0.88 1 450
  1. AC- Amino acid composition, DC dipeptide composition, PSSM position specific scoring matrix, ACC accuracy, SN- Sensitivity, SP- specificity, MCC- Mathews correlation coefficient,C: tradeoff value, γ- gamma factor (a parameter in RBF kernel).
  2. SAK - Staphylokinase, SK - Streptokinase, tPA - tissue plasminogen activators, UK - Urokinase.