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
|
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 |
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.