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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