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Table 9 The effect of noise level on GRN reconstruction in case of non-linearity between the genes

From: Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network

  Methods σ 2 TP FP FN Precision Recall F-score Nominal p-value Adjusted p-value
Delay HRNN 0.1 18.7 34.2 20.5 0.35 0.47 0.41   
   0.25 18.5 31.9 22.0 0.36 0.45 0.41   
   0.5 15.6 34.4 23.6 0.31 0.39 0.34   
   1 8.0 37.8 31.6 0.17 0.20 0.18   
   1.5 4.5 37.3 35.0 0.11 0.11 0.11   
Delay HCC-CLINDE 0.1 18.3 10.2 20.9 0.64 0.46 0.53 0.01 0.05
   0.25 17.9 10.4 22.6 0.63 0.44 0.51 0.005 0.025
   0.5 12.7 8.5 26.5 0.60 0.32 0.42 0.03 0.15
   1 3.9 9.6 35.7 0.28 0.09 0.14 0.27 1
   1.5 0.4 7.4 39.1 0.05 0.01 0.01 3.9×10−6 1.9×10−5
Effect HRNN 0.1 19.8 29.3 19.8 0.40 0.50 0.44   
   0.25 19.2 27.6 21.9 0.41 0.46 0.43   
   0.5 16.1 30.7 23.5 0.34 0.40 0.37   
   1 9.8 32.3 29.9 0.23 0.24 0.23   
   1.5 5.1 33.3 34.5 0.13 0.12 0.13   
Effect HCC-CLINDE 0.1 18.5 10.0 20.7 0.64 0.47 0.54 0.06 0.3
   0.25 18.0 10.3 22.5 0.63 0.44 0.52 0.02 0.1
   0.5 12.7 8.5 26.5 0.60 0.32 0.42 0.16 0.8
   1 3.9 9.6 35.7 0.28 0.09 0.14 0.02 0.1
   1.5 0.4 7.4 39.1 0.05 0.01 0.01 3.4×10−7 1.7×10−6
  1. Results are the average of accuracy of the Delay and Effect criterion for GRN reconstruction of 10 different randomly generated synthetic networks. The p-values are for how the Delay and Effect F-scores of HCC-CLINDE method compare with HRNN. σ 2 is variance of the noise. Networks include 20 genes