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Table 2 Performance comparison of CTBNs, DBNs and GC on simulated data for different time granularities

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

Method Time course granularity Mean precision Mean recall Mean F 1 F 1 SEM
GC 11 0.43 0.61 0.50 2.88E-02
  21 0.40 0.70 0.49 3.35E-02
  31 0.35 0.75 0.47 3.80E-02
DBNs 11 0.84 0.15 0.26 4.33E-02
  21 0.55 0.42 0.47 3.66E-02
  31 0.68 0.30 0.40 2.79E-02
CTBNs 11 0.70 0.36 0.47 2.05E-02
  21 0.72 0.48 0.57 5.54E-02
  31 0.59 0.51 0.54 3.23E-02
Method Time course granularity Mean precision Mean recall Mean F 1 F 1 SEM
GC 11 0.47 0.76 0.57 4.05E-02
  21 0.28 0.81 0.41 5.78E-02
  31 0.29 0.80 0.42 3.56E-02
DBNs 11 0.76 0.21 0.32 2.32E-02
  21 0.60 0.57 0.58 4.31E-02
  31 0.63 0.40 0.48 3.86E-02
CTBNs 11 0.60 0.53 0.60 3.25E-02
  21 0.72 0.70 0.70 6.03E-02
  31 0.56 0.67 0.60 3.48E-02
  1. Tests refer to 20NETs, 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 4.
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