Skip to main content

Table 3 Subtype prediction accuracy of 5 gene lists in the GB, BC and OC validation datasets

From: Splitting random forest (SRF) for determining compact sets of genes that distinguish between cancer subtypes

Cancer

Methods

Classical (N = 50)

Mesenchymal (N = 48)

Neural (N = 30)

Proneural (N = 48)

GB

SRF50

70.0%

81.3%

73.3%

93.8%

 

Single RF

64.0%

77.1%

80.0%

91.7%

 

Verhaak et al.

88.0%

91.7%

50.0%

100.0%

 

ANOVA

88.0%

91.7%

40.0%

100.0%

 

Top 50 ANOVA

66.0%

83.3%

73.3%

87.5%

  

Basal-Like (N = 18)

HER2 (N = 11)

Luminal A (N = 20)

Luminal B (N = 32)

BC

SRF50

100.0%

63.6%

80.0%

84.4%

 

Single RF

88.9%

72.7%

55.0%

87.5%

 

Parker et al.

100.0%

54.5%

90.0%

93.8%

 

ANOVA

100.0%

36.4%

85.0%

93.8%

 

Top 50 ANOVA

100.0%

63.6%

75.0%

84.4%

  

C1 (N = 42)

C2 (N = 25)

C4 (N = 23)

C5 (N = 18)

OC

SRF50

97.6%

88.0%

73.9%

94.4%

 

Single RF

97.6%

88.0%

78.3%

83.3%

 

Tothill et al.

97.6%

88.0%

87.0%

88.9%

 

ANOVA

100.0%

88.0%

78.3%

88.9%

 

Top 50 ANOVA

95.2%

88.0%

73.9%

83.3%