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Table 4 The mean and standard deviation (in parenthesis) of sensitivity, specificity and area under curve (AUC) calculated for conventional differential gene expression analysis: DEA, dwgLASSO with no prior biological knowledge incorporated: dwgLASSO (no prior), KDDN, and dwgLASSO with prior biological knowledge incorporated: dwgLASSO (prior)

From: Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

Methods Testing dataset 1 Testing dataset 2
  Specificity Sensitivity AUC Specificity Sensitivity AUC
DEA 0.950 (0.07) 0.913 (0.06) 0.951 (0.04) 0.950 (0.07) 0.941 (0.04) 0.983 (0.01)
dwgLASSO (no prior) 0 . 9 8 8 ( 0 . 0 3 ) 0.888 (0.11) 0.972 (0.02) 0 . 9 8 8 ( 0 . 0 3 ) 0.956 (0.05) 0.990 (0.01)
KDDN 0.963 (0.08) 0 . 9 5 0 ( 0 . 0 4 ) 0.980 (0.02) 0.963 (0.08) 0.939 (0.03) 0.989 (0.01)
dwgLASSO (prior) 0 . 9 8 8 ( 0 . 0 3 ) 0.950 (0.07) 0 . 9 8 2 ( 0 . 0 3 ) 0 . 9 8 8 ( 0 . 0 3 ) 0 . 9 6 5 ( 0 . 0 3 ) 0 . 9 9 4 ( 0 . 0 1 )
  1. The best performance is marked in bold
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BMC Bioinformatics

ISSN: 1471-2105

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