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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.
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