Table 5 Performances of the various GRN inference methods on the datasources. AUPR in the top 20 % of the possible connections with a undirected evaluation for each GRN inference method on the different datasources of the benchmark with a 20 % local Gaussian noise and 10 % of global lognormal noise. The best statistically significant results tested with a Wilcoxon test are highlighted for each datasource. Results obtained with current version (1.0) of the package and are updated online

R1

mean

0.004

0.002

0.005

0.140

0.024

0.005

0.042

0.005

0.177

0.140

<0.001

σ(×10⁻³)

1.1

0.789

1.22

16

2.97

1.26

7.27

1.26

16.1

13.6

0.0265

S1

mean

0.039

0.032

0.139

0.062

0.134

0.109

0.063

0.118

0.060

0.028

0.001

σ(×10⁻³)

8.02

7.92

1.98

8.25

3.51

9.45

2.25

5.83

1.44

13.8

0.211

S2

mean

0.006

0.006

0.042

0.013

0.036

0.021

0.021

0.021

0.01

0.003

<0.001

σ(×10⁻³)

1.19

1.63

1.55

1.56

1

2.76

0.959

2.01

0.522

1.46

0.1

G1

mean

0.106

0.100

0.139

0.085

0.108

0.134

0.034

0.084

0.063

0.001

<0.001

σ(×10⁻³)

7.46

7.58

7.83

2.91

6.66

9.48

2.26

3.27

2.69

0.15

0.0141

G2

mean

0.101

0.095

0.106

0.037

0.069

0.126

0.025

0.058

0.044

<0.001

<0.001

σ(×10⁻³)

11.4

9.95

4.49

1.62

3.44

9.49

1.43

2.23

2.16

0.0917

0.0265