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Table 1 Performance metrics of different ML algorithms using the combined features ATC + P2G + AAC_PSSM + D-FPSSM when applied to exactly the same dataset (positives and sampled negatives) used by Wei et al. [22]

From: Improved cytokine–receptor interaction prediction by exploiting the negative sample space

  SE SP ACC MCC AUC g-means
ATC + P2G + AAC_PSSM + D-FPSSM
 NB 94.1 68.5 81.3 0.647 0.843 80.2
 A1DE 95.1 69.5 82.3 0.668 0.910 81.2
 SMO-RBF 93.6 86.7 90.1 0.805 0.901 90.1
 SMO-PolyK 92.6 77.3 85.0 0.708 0.850 84.6
 SMO-PuK 90.6 88.2 89.4 0.788 0.894 89.4
 IBK 91.6 84.2 87.9 0.761 0.879 87.8
 Bagging 85.7 79.8 82.8 0.656 0.904 82.7
 RF 89.2 81.3 85.2 0.707 0.935 85.1
 Wei et al 92.6 83.3 87.9 0.762 87.8
  1. For comparison, we also show the metrics reported by Wei et al. The best performance for each metric is shown in italic