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Table 7 Results of the CNS dataset from different strategies.

From: Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique

  mAChR4 c-jun GFAP IP32R c-fos ChAT
  w/o with w/o with w/o with w/o with w/o with w/o with
Fitness 0.0014 0.0006 0.0006 0.0001 0.0004 0.0001 0.0008 0.0003 0.0016 0.0004 0.0009 0.0003
Sensitivity 0.7828 0.7595 0.7262 0.7019 0.7494 0.7149 0.7358 0.7153 0.7877 0.7395 0.7697 0.7383
  NMDA2A MOG bFGF S100 beta mGluR1 CNTF
  w/o with w/o with w/o with w/o with w/o with w/o with
Fitness
sensitivity
0.0008 0.0002 0.0007 0.0002 0.0028 0.0001 0.0012 0.0002 0.0013 0.0004 0.0008 0.0001
0.7672 0.7373 0.7619 0.7578 0.7831 0.7557 0.7885 0.7444 0.7232 0.6939 0.8074 0.7313  
  NEFH GRG1 NFM NGF aFGF   
  w/o with w/o with w/o with w/o with w/o with   
Fitness 0.0021 0.0007 0.0017 0.0002 0.0048 0.0015 0.0144 0.0037 0.0152 0.0068   
Sensitivity 0.7367 0.7178 0.8696 0.8039 0.7875 0.7378 0.7725 0.7574 0.7689 0.7433