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Table 1 Classification prediction results of the simulation

From: A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression

S

\(\rho\)

Lasso (%)

M-Lasso (%)

EN (%)

M-EN (%)

HLR (%)

M-HLR (%)

SGL (%)

Meta-Lasso (%)

DSL (%)

DA-DSL-\({\mathrm{L}}_{2}\) (%)

Accuracy

1

0.3

72.85

80.30

76.65

80.13

74.32

80.18

82.03

82.17

81.39

84.19

 

0.6

57.32

74.89

59.47

77.47

56.30

80.41

81.52

80.54

79.88

80.83

 

0.9

58.18

74.55

58.62

75.10

60.88

76.19

79.50

79.60

82.00

85.68

2

0.3

51.73

71.55

55.26

71.53

56.60

70.95

75.79

76.34

75.52

76.37

 

0.6

54.80

71.71

59.73

72.10

56.94

72.39

76.58

73.19

75.41

77.56

 

0.9

55.96

71.19

60.30

74.70

53.12

72.67

77.01

75.80

77.03

78.63

3

0.3

59.08

69.29

61.22

69.10

59.44

68.48

72.34

70.59

74.77

75.19

 

0.6

60.01

75.04

63.02

73.27

63.67

71.79

74.55

75.02

75.35

79.22

 

0.9

67.02

70.06

70.05

69.63

68.86

71.99

74.69

72.68

73.01

75.70

4

0.3

81.70

84.00

83.58

83.46

80.22

82.02

85.33

83.51

85.88

87.58

 

0.6

50.01

71.38

53.99

72.96

66.21

73.34

74.74

72.96

74.62

76.91

 

0.9

61.07

70.32

65.95

71.27

64.97

74.89

80.83

78.71

79.35

83.50

  1. *S denotes the Scenario and \(\rho\) is the correlation control variable of data. In bold – the best performance amongst all the methods