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Table 2 Performances of lasso, elastic-net (EN), and ridge MVAR models on synthetic datasets with varying numbers of genes and time points.

From: Stability of building gene regulatory networks with sparse autoregressive models

Number of genes Number of time points Method True positives False positives Precision Recall F-measure Stability
   EN 5.86 7.94 0.47 0.65 0.53 0.41
  10 Lasso 5.19 4.88 0.57 0.58 0.55 0.46
   Ridge 8.99 90.70 0.09 1.00 0.16 0.99
   EN 8.66 8.02 0.56 0.96 0.69 0.64
  30 Lasso 8.54 5.79 0.63 0.95 0.75 0.65
   Ridge 8.99 90.55 0.09 1.00 0.16 0.99
   EN 8.99 5.91 0.64 1.00 0.77 0.71
10 50 Lasso 8.99 4.12 0.71 1.00 0.82 0.75
   Ridge 8.98 90.53 0.09 1.00 0.16 0.99
   EN 9.00 5.12 0.67 1.00 0.79 0.72
  70 Lasso 8.99 3.92 0.72 1.00 0.83 0.76
   Ridge 8.99 90.35 0.09 1.00 0.16 0.99
   EN 17.29 131.50 0.12 0.35 0.18 0.15
  10 Lasso 13.53 42.81 0.25 0.28 0.26 0.15
   Ridge 48.90 2429.90 0.02 1.00 0.04 0.99
   EN 32.73 95.75 0.26 0.67 0.37 0.27
  30 Lasso 32.37 72.41 0.31 0.66 0.42 0.31
   Ridge 48.90 2431.70 0.02 1.00 0.04 0.99
   EN 39.39 77.88 0.34 0.80 0.48 0.35
50 50 Lasso 39.15 0.39 0.80 0.82 0.52 0.38
   Ridge 48.9 2436.20 0.02 1.00 0.04 0.99
   EN 48.82 98.11 0.34 1.00 0.50 0.41
  70 Lasso 48.82 78.38 0.39 1.00 0.56 0.45
   Ridge 49.00 2432.90 0.02 1.00 0.04 0.99
   EN 22.33 259.46 0.08 0.23 0.12 0.08
  10 Lasso 14.91 69.95 0.18 0.15 0.16 0.08
   Ridge 98.31 9759.90 0.01 1.00 0.02 0.98
   EN 58.67 214.17 0.22 0.59 0.32 0.20
  30 Lasso 56.37 153.76 0.27 0.57 0.37 0.22
   Ridge 98.71 9777.50 0.01 1.00 0.02 0.99
   EN 79.10 190.56 0.30 0.80 0.43 0.29
100 50 Lasso 78.60 164.55 0.33 0.79 0.46 0.32
   Ridge 98.80 9792.20 0.01 1.00 0.02 0.99
   EN 87.62 160.01 0.36 0.89 0.51 0.36
  70 Lasso 87.54 144.63 0.38 0.88 0.53 0.38
   Ridge 98.81 9805 0.01 1.00 0.02 0.99