Skip to main content

Table 6 The effect of network size on GRN reconstruction in case of non-linearity between the genes. Results are the average of the accuracy in terms of the Delay and Effect criterion for GRN reconstruction of 10 different randomly generated synthetic networks

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

  Methods P TP FP FN Precision Recall F-score Nominal p-value Adjusted p-value
Delay HRNN 5 4.7 7.0 5.6 0.40 0.45 0.42   
   10 3.9 19.0 15.1 0.17 0.19 0.18   
   20 8.0 37.8 31.6 0.17 0.20 0.18   
   30 9.0 55.4 48.0 0.14 0.16 0.15   
Delay HCC-CLINDE 5 2.6 2.3 7.7 0.57 0.25 0.34 0.25 1
   10 2.6 2.3 7.7 0.57 0.25 0.34 0.69 1
   20 3.9 9.6 35.7 0.28 0.09 0.14 0.27 1
   30 3.3 13.1 53.7 0.21 0.05 0.08 0.03 0.12
Effect HRNN 5 4.8 4.3 6.0 0.52 0.44 0.47   
   10 4.6 15.0 14.5 0.23 0.23 0.23   
   20 9.8 32.3 29.9 0.23 0.24 0.23   
   30 9.9 51.7 47.3 0.16 0.17 0.17   
Effect HCC-CLINDE 5 2.7 2.2 7.6 0.59 0.26 0.35 0.08 0.32
   10 3.1 4.6 15.9 0.43 0.16 0.23 0.95 1
   20 3.9 9.6 35.7 0.28 0.09 0.14 0.02 0.08
   30 3.4 13.0 53.6 0.21 0.05 0.09 0.01 0.04
  1. The p-values are for how the Delay and Effect F-scores of HCC-CLINDE method compare with HRNN. P is the number of genes for each of the networks. The variance of the noise is equal to 1