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Table 8 Prediction sensitivities and specificities for the two cohorts, A and B

From: Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models

Type Method CV (A/ B) AB BA Mean G-perf
   Se Sp Se Sp Se Sp Test sets
  tForwardLd 0.38/0.63 0.80/0.65 0.50 0.82 1.00 0.88 0.79
  From Arijs, 2009 Accuracy: 0.92/0.91 0.25 1 Accuracy: 0.71 na
All Genes RF 0.20/0.82 0.88/0.73 0.25 0.91 0.62 0.75 0.58
  SVM 0.52/0.78 0.85/0.69 0.42 0.82 0.62 0.75 0.63
  NSC 0.48/0.78 0.80/0.58 0.67 1.00 0.69 0.88 0.80
  tForwardLd 0.50/0.68 0.83/0.62 0.67 1.00 0.88 0.69 0.80
  LDA 0.43/0.90 0.84/0.65 0.42 0.82 0.88 0.62 0.66
Gene in transcript layer NSC 0.60/0.88 0.79/0.58 0.75 0.73 0.88 0.56 0.72
  RF 0.28/0.85 0.88/0.71 0.33 0.82 0.88 0.69 0.65
  SVM 0.52/0.77 0.86/0.69 0.42 0.82 0.88 0.69 0.68
  CORG + LDA 0.33/0.73 0.68/0.62 0.25 0.64 0.75 0.69 0.56
  CORG + NSC 0.62/0.90 0.78/0.60 0.50 0.82 0.88 0.69 0.71
UBE downstream genes Best LDA (MAP3K1) 0.45/0.98 0.78/0.69 0.58 0.91 1.00 0.75 0.80
  Best NSC (catof(TLR2)) 0.85/0.92 0.80/0.56 0.75 1.00 1.00 0.69 0.85
  tForwardLd 0.53/0.82 0.75/0.75 0.50 0.64 1.00 0.62 0.68
  LDA 0.75/0.75 0.76/0.75 0.50 0.73 0.88 0.56 0.65
Backbone values NSC 0.98/0.98 0.75/0.67 0.92 0.82 1.00 0.62 0.83
  SVM 0.78/0.78 0.40/0.77 0.91 0.50 0.69 0.88 0.73
  RF 0.75/0.82 0.62/0.78 0.82 0.75 0.75 0.88 0.80
  1. The predictions of the samples from the cohort A (B respectively) based on the model trained on the cohorts B (A respectively) are reported in the columns AB (BA respectively). The mean of the G-performances ( = Sp · Se ) over the two independent test sets are shown in the rightmost column and is highlighted if >0.7. The best UBE-based models is chosen based on the mean cross-validation G-performance for the two datasets. The algorithms based on the backbone values leads to good performances for a majority of algorithms. The cross-validation standard errors are reported in Additional file 1: Table S2.
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