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Table 2 Multiple Kernel AUPRC values on gold standard data sets in the pairwise cross-validation setting (maximum values are denoted by bold face (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

Neighbors

MrgRbf

MrgTan

McsRbf

McsTan

Orig

All

Nuclear Receptor (KBMF-MKL: 0.566, KronRLS-MKL: 0.522)

 2

0.749

0.758

0.742

0.735

0.754

0 . 7 7 9

 3

0.744

0.771

0.761

0.734

0.773

0.775

 5

0.732

0.757

0.739

0.724

0.755

0.756

 2+3

0.750

0.765

0.754

0.736

0.757

0.758

 2+3+5

0.760

0.765

0.740

0.738

0.764

0.760

GPCR (KBMF-MKL: 0.622, KronRLS-MKL: 0.696)

 2

0.743

0.759

0.754

0.762

0.764

0.793

 3

0.755

0.774

0.772

0.780

0.777

0 . 8 0 2

 5

0.762

0.787

0.782

0.783

0.787

0.796

 2+3

0.763

0.782

0.781

0.786

0.785

0 . 8 0 2

 2+3+5

0.777

0.798

0.793

0.789

0.796

0.800

Ion Channel (KBMF-MKL: 0.826, KronRLS-MKL: 0.885)

 2

0.909

0.911

0.910

0.911

0.910

0.909

 3

0.911

0.914

0.915

0.914

0.912

0.916

 5

0.915

0.914

0.913

0.916

0.916

0 . 9 1 7

 2+3

0.912

0.914

0.916

0.914

0.913

0.909

 2+3+5

0.912

0.915

0.915

0.915

0.916

0.906

Enzyme (KBMF-MKL: 0.704, KronRLS-MKL: 0.893)

 2

0.885

0.887

0.879

0.883

0.888

0.884

 3

0.885

0.890

0.885

0.882

0.890

0 . 8 9 5

 5

0.883

0.886

0.880

0.881

0.884

0.883

 2+3

0.888

0.889

0.880

0.881

0.888

0.881

 2+3+5

0.887

0.889

0.881

0.878

0.888

0.875

Kinase (KBMF-MKL: 0.846, KronRLS-MKL: 0.561)

Neighbors

-

2D

3D

ECFP

All

 2

 

0.850

0.849

0.849

0.850

 3

 

0.850

0.848

0.850

0.851

 5

-

0.850

0.849

0.850

0.851

 2+3

 

0.850

0.850

0.850

0.853

 2+3+5

 

0.851

0.851

0.850

0 . 8 5 4

  1. The table headers indicate the best AUPRC values obtained using the KBMF-MKL and KronRLS-MKL tools, utilizing all kernels and a grid search method for parameterization. The table bodies show AUPRC values from the VB-MK-LMF method in a cumulative manner. In particular, rows correspond to the cut-off value of the number of closest neighbors and the combinations of the resulting truncated kernels. Columns correspond to individual kernels. The last column was obtained by combining all kernels