Skip to main content

Table 3 Fitness and sensitivity obtained from different settings.

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

 

PSO

GA-PSO

 

dataset 1

dataset 2

dataset 1

dataset 2

 

w/o

with

w/o

with

w/o

with

w/o

with

Avg

2.02E-03

1.17E-03

8.69E-02

4.62E-02

4.34E-04

1.45E-04

3.04E-02

1.23E-02

Best

2.97E-04

5.79E-04

1.76E-02

1.39E-02

2.47E-04

4.90E-05

2.42E-03

6.66E-04

Worst

5.50E-03

2.72E-03

2.46E-01

1.42E-01

1.57E-03

4.62E-04

9.51E-02

5.76E-02

S.D.

1.79E-03

5.48E-04

6.42E-02

2.98E-02

2.45E-04

9.56E-05

3.29E-02

1.70E-02

Sensitivity

0.7703

0.7281

0.6929

0.6468

0.7844

0.7388

0.7684

0.7462

Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us