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Table 1 The performance of various SVM models of Pg-activators (SAK, SK, tPA and UK) with non Pg- activators, was developed using AC, DC,PSSM profiles and Hybrid models in five-fold cross validation

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

  ACC(%) SN(%) SP(%) MCC Parameters
Methods      γ C
AC 88.37 95.24 83.50 0.87 25 450
DC 84.32 97.01 75.31 0.83 3 375
PSSM 87.61 95.77 81.81 0.86 3 400
Hybrid (AC + DC) 85.63 97.71 77.06 0.85 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).