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Table 2 Comparison between SCADDx and LOADDx using CTD KB

From: Enabling personalised disease diagnosis by combining a patient’s time-specific gene expression profile with a biomedical knowledge base

Datasets

Algorithm

Parameter

values

Accuracy

n@1 (%)

Accuracy

n@2 (%)

Accuracy

n@3 (%)

Accuracy

n@4 (%)

Accuracy

n@5 (%)

Accuracy

n@10 (%)

Dataset 1

Testset 1a

(GSE73072)

SCADDx

P = 200

Q = 200

76.92

76.92

76.92

76.92

76.92

76.92

LOADDx

P = 200

Q = 200

69.23

69.23

69.23

69.23

69.23

69.23

Dataset 1

Testset 1b

(GSE73072)

SCADDx

P = 200

Q = 200

84.62

84.62

84.62

84.62

84.62

76.92

LOADDx

P = 200

Q = 200

76.92

76.92

76.92

76.92

76.92

76.92

Dataset 2

Testset 2a

(GSE68310)

SCADDx

P = 200

Q = 200

100

100

100

100

100

100

LOADDx

P = 200

Q = 200

75

75

87.5

87.5

87.5

100

Dataset 2

Testset 2b

(GSE68310)

SCADDx

P = 200

Q = 200

86.66

86.66

86.66

86.66

86.66

93.33

LOADDx

P = 200

Q = 200

86.66

86.66

86.66

86.66

86.66

86.66

Dataset 3

(GSE90732)

SCADDx

P = 200

Q = 200

100

100

100

100

100

100

LOADDx

P = 200

Q = 200

100

100

100

100

100

100

Dataset 4

(GSE61754)

SCADDx

P = 200

Q = 200

85.71

85.71

85.71

85.71

85.71

85.71

LOADDx

P = 200

Q = 200

85.71

85.71

85.71

85.71

85.71

85.71

  1. Parameter values: P = Q = 200 genes for all the four datasets