Table 5.
Results of the different inference methods on DREAM5 networks
| Method |
Network (AUPR/AUROC respectively) |
Overall | |||||
|---|---|---|---|---|---|---|---|
| 1 | 3 | 4 | |||||
|
Experimental results | |||||||
| ENNET |
0.432 |
0.867 |
0.069 |
0.642 |
0.021 |
0.532 |
>300 |
| ADANET |
0.261 |
0.725 |
0.083 |
0.596 |
0.021 |
0.517 |
16.006 |
| GENIE3 |
0.291 |
0.814 |
0.094 |
0.619 |
0.021 |
0.517 |
40.335 |
| C3NET |
0.080 |
0.529 |
0.026 |
0.506 |
0.018 |
0.501 |
0.000 |
| CLR |
0.217 |
0.666 |
0.050 |
0.538 |
0.019 |
0.505 |
4.928 |
| MRNET |
0.194 |
0.668 |
0.041 |
0.525 |
0.018 |
0.501 |
2.534 |
| ARACNE |
0.099 |
0.545 |
0.029 |
0.512 |
0.017 |
0.500 |
0.000 |
| Winner of the challenge | |||||||
| GENIE3 |
0.291 |
0.815 |
0.093 |
0.617 |
0.021 |
0.518 |
40.279 |
| ANOVA η2 |
0.245 |
0.780 |
0.119 |
0.671 |
0.022 |
0.519 |
34.023 |
| TIGRESS | 0.301 | 0.782 | 0.069 | 0.595 | 0.020 | 0.517 | 31.099 |
Results of the different inference methods on DREAM5 networks. An area under the ROC curve (AUROC) and an area under the Precision-Recall curve (AUPR) are given for each network respectively. The Overall Score for all the networks is given in the last column. The best results for each column are in bold. Numbers in the “Experimental results” part of the table were collected after running the algorithms with the default sets of parameters on pre-processed data. Numbers in the “Winner of the challenge” part of the table correspond to the best methods participating in the challenge.