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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

From: NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference

Datasource ARACNE C3NET CLR GeneNet Genie3 MRNET MutRank MRNETB PCIT Zscore Random
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
  1. p < 0.05