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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

  
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BMC Bioinformatics

ISSN: 1471-2105

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