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Table 1 Performance comparison of BVSA, (stochastic)MRA, SBRA and LMML algorithms along with the winners in the 10 and 100 gene categories ([35],[36]) of the DREAM4 challenge

From: Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

Algorithm 10 Gene network 100 Gene network
  AUROC AUPR Time (secs) AUROC AUPR Time (secs)
BVSA 0.9323 ± 0.0121 0.7311 ± 0.011 6.023 ± 0.119 0.85 ± 0.0101 0.14 ± 0.0108 1384.92 ± 12.8
stochastic MRA 0.9231 0.7133 0.0008 0.709 0.037 0.68
SBRA 0.7572 ± 0.019 0.58 ± 0.02 0.11 ± 0.02 0.65 ±0.003 0.075 ±0.01 1520 ± 3.319
LMML 0.8035 ± 0.06 0.66 ± 0.07 27.32 ± 1.73 0.644 ±0.02 0.04 ±0.001 41562 ± 3722.2
Kuffer et. al.[36] 0.972 0.916 NA NA NA NA
Pinna et. al. [35] 0.764 0.590 NA 0.914 0.536 NA
  1. The results are shown in mean ± std format. The information regarding the performance of Kuffner et. al.’s algorithm on the 100 gene dataset is not available since they did not participate in the 100 gene category of the DREAM4 challenge. The execution times of Pinna et. al.’s amd Kuffer et. al.’s algorithms were not published and therefore not available. Unavailble information is shown by ‘NA’ in the table.