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Table 1 Single-kernel results on gold standard data sets (maximum values are denoted by bold face)

From: VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

  VB-MK-LMF NRLMF KBMF
AUROC (CV1)
 Nuclear Receptor 0 . 9 5 7±0.010 0.949±0.011 0.860±0.024
 GPCR 0 . 9 7 6±0.003 0.960±0.004 0.911±0.004
 Ion Channel 0 . 9 8 9±0.001 0.984±0.002 0.941±0.003
 Enzyme 0 . 9 8 7±0.001 0.976±0.002 0.887±0.003
 Kinase 0 . 9 2 1±0.002 0.919±0.001 0.916±0.001
AUPRC (CV1)
 Nuclear Receptor 0 . 7 7 3±0.030 0.723±0.042 0.533±0.047
 GPCR 0 . 7 7 7±0.016 0.703±0.023 0.541±0.012
 Ion Channel 0 . 9 1 6±0.007 0.863±0.012 0.763±0.009
 Enzyme 0 . 8 9 0±0.006 0.876±0.007 0.656±0.008
 Kinase 0 . 8 5 0±0.003 0.845±0.003 0.844±0.003
AUROC (CV2)
 Nuclear Receptor 0 . 9 3 9±0.021 0.896±0.023 0.845±0.023
 GPCR 0.878±0.014 0 . 8 8 3±0.012 0.847±0.018
 Ion Channel 0 . 8 1 2±0.026 0.800±0.026 0.785±0.021
 Enzyme 0 . 8 5 1±0.021 0.811±0.024 0.718±0.028
 Kinase 0 . 8 9 4±0.004 0.891±0.004 0.838±0.004
AUPRC (CV2)
 Nuclear Receptor 0 . 5 9 3±0.058 0.547±0.053 0.447±0.048
 GPCR 0 . 3 6 8±0.023 0.363±0.023 0.365±0.024
 Ion Channel 0 . 3 4 5±0.035 0.343±0.033 0.287±0.035
 Enzyme 0.349±0.042 0 . 3 6 0±0.041 0.269±0.037
 Kinase 0 . 8 0 3±0.009 0.797±0.010 0.735±0.009
AUROC (CV3)
 Nuclear Receptor 0 . 9 1 7±0.026 0.847±0.029 0.735±0.050
 GPCR 0 . 9 4 1±0.009 0.920±0.014 0.839±0.020
 Ion Channel 0 . 9 6 6±0.007 0.958±0.008 0.911±0.012
 Enzyme 0 . 9 6 2±0.005 0.947±0.006 0.859±0.012
 Kinase 0 . 7 6 7±0.018 0.763±0.018 0.740±0.022
AUPRC (CV3)
 Nuclear Receptor 0 . 6 0 1±0.081 0.456±0.079 0.352±0.070
 GPCR 0 . 5 9 6±0.040 0.553±0.040 0.437±0.047
 Ion Channel 0 . 8 2 6±0.021 0.788±0.028 0.695±0.024
 Enzyme 0.794±0.017 0 . 8 0 8±0.018 0.573±0.028
 Kinase 0 . 6 0 8±0.039 0.597±0.038 0.594±0.039
  1. CV indicates the cross-validation setting (pairwise, drug and target, respectively). AUROC and AUPRC values were averaged over 5×10 runs and 95% confidence intervals were computed. In most cases, VB-MK-LMF significantly outperforms the other methods using t-test