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Table 2 Effects of sequentially adding data sources

From: Enhanced protein fold recognition through a novel data integration approach

Data sources

MKLdiv-dc

MKLdiv-conv

VBKC

SimpleMKL

MKL-RKDA

C

51.69

51.69

51.2 ± 0.5

51.69

47.25

CS

56.39

(20.23 s)

55.35

(0.32 s)

55.7 ± 0.5

(-)

55.61

(14.67 s)

48.30

(0.15 s)

CSH

57.70

(50.35 s)

58.22

(2.44 s)

57.7 ± 0.6

(-)

56.91

(10.40 s)

55.61

(0.12 s)

CSHP

58.48

(39.02 s)

53.52

(72.14 s)

57.9 ± 0.9

(-)

57.96

(17.84 s)

56.65

(0.18 s)

CSHPV

60.05

(75.05 s)

53.26

(86.39 s)

58.1 ± 0.8

(-)

57.96

(15.05 s)

55.87

(0.17 s)

CSHPVZ

59.26

(135.08 s)

53.52

(99.64 s)

58.6 ± 1.1

(-)

59.00

(20.02 s)

57.70

(0.20 s)

CSHPVZL1

60.05

(221.75 s)

52.74

(122.74 s)

60.0 ± 0.8

(-)

61.35

(27.38 s)

57.70

(0.21 s)

CSHPVZL1L4

62.14

(315.70 s)

52.74

(129.08 s)

60.8 ± 1.1

(-)

61.61

(151.38 s)

58.22

(0.25 s)

CSHPVZL1L4L14

62.14

(450.57 s)

61.09

(57.09 s)

61.5 ± 1.2

(-)

60.05

(42.81 s)

59.53

(0.25 s)

CSHPVZL1L4L14L30

62.14

(612.72 s)

62.14

(67.29 s)

62.2 ± 1.3

(-)

62.40

(64.74 s)

55.61

(0.25 s)

CSHPVZL1L4L14L30SW1

71.80

(620.16 s)

71.54

(17.97 s)

66.4 ± 0.8

(-)

65.79

(78.94 s)

66.84

(0.31 s)

CSHPVZL1L4L14L30SW1SW2

73.36

(805.11 s)

71.01

(84.21 s)

68.1 ± 1.2

(-)

66.57

(196.42 s)

68.40

(0.31 s)

SHPVZL1L4L14L30

60.57

(438.89 s)

61.09

(67.92 s)

61.1 ± 1.4

(-)

59.00

(44.79 s)

54.56

(0.25 s)

  1. The result of Bayesian kernel learning model (VBKC) is cited from [21]. The results not employed there are denoted by '-'. The term inside the parenthesis is the CPU running time (seconds). The best test set accuracy of each kernel learning method is marked in bold.