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Table 1 Performance comparison of CTBNs, DBNs and GC on simulated data for different network sizes

From: Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Method NETs size Mean precision Mean recall Mean F 1 F 1 SEM
GC 10 0.46 0.68 0.54 6.40E-02
  20 0.40 0.70 0.49 4.33E-02
  50 0.24 0.82 0.37 3.23E-02
  100 0.16 0.82 0.27 2.13E-02
DBNs 10 0.90 0.29 0.41 6.90E-02
  20 0.55 0.42 0.47 3.66E-02
CTBNs 10 0.66 0.58 0.61 5.13E-02
  20 0.72 0.48 0.57 2.79E-02
  50 0.53 0.57 0.54 1.95E-02
  100 0.45 0.51 0.48 2.28E-02
Random 10 0.16 0.55 0.24 2.12E-02
  20 0.11 0.51 0.18 1.68E-02
  50 0.03 0.49 0.06 4.35E-03
  100 0.02 0.50 0.04 1.15E-03
Method NETs size Mean precision Mean recall Mean F 1 F 1 SEM
GC 10 0.42 0.75 0.52 4.18E-02
  20 0.28 0.81 0.41 2.32E-02
  50 0.22 0.78 0.34 1.58E-02
  100 0.14 0.80 0.23 5.24E-03
DBNs 10 0.62 0.53 0.56 3.40E-02
  20 0.60 0.57 0.58 4.31E-02
CTBNs 10 0.95 0.58 0.69 6.08E-02
  20 0.72 0.70 0.70 3.86E-02
  50 0.64 0.56 0.59 3.84E-02
  100 0.56 0.51 0.53 2.65E-02
Random 10 0.18 0.59 0.27 2.10E-02
  20 0.07 0.49 0.12 1.27E-02
  50 0.05 0.50 0.08 4.88E-03
  100 0.02 0.50 0.05 2.63E-03
  1. Organism E.coli (top) and S. cerevisiae (bottom). Aggregate F 1 , precision and recall values are calculated as the arithmetic mean over the sets of 10 sampled network instances, the standard error of the F 1 mean (SEM) is also shown. See also Figure 3.
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