Table 1. Performance Evaluation of the Top 200,000 Predictions of the Four Individual Reverse-Engineering Methods and Their Ensemble Solutions on Correctly Predicting Known Regulatory Interactions.
| Npred | TP | Precision | Recall | F | AUPR | |
|---|---|---|---|---|---|---|
| Set of 52,328 known protein-DNA and/or regulatory interactions | ||||||
| LeMoNe_qopt25R | 31886 | 972 | 0.030 | 0.019 | 0.023 | 0.00118 |
| LemoNe_qopt50R | 30056 | 862 | 0.029 | 0.016 | 0.021 | 0.00104 |
| ClrR | 31290 | 1301 | 0.042 | 0.025 | 0.031 | 0.00190 |
| TwixTrixR | 26353 | 955 | 0.036 | 0.018 | 0.024 | 0.00072 |
| Union | 31847 | 1092 | 0.034 | 0.021 | 0.026 | 0.00130 |
| Rank_rp | 31824 | 1091 | 0.034 | 0.021 | 0.026 | 0.00137 |
| Rank_av | 31546 | 1182 | 0.037 | 0.023 | 0.028 | 0.00158 |
| Extended set of 789,068 known and “hidden” interactions | ||||||
| LeMoNe_qopt25R | 31886 | 10696 | 0.335 | 0.014 | 0.026 | 0.00516 |
| LemoNe_qopt50R | 30056 | 8686 | 0.289 | 0.011 | 0.021 | 0.00352 |
| ClrR | 31290 | 10056 | 0.321 | 0.013 | 0.025 | 0.00466 |
| TwixTrixR | 26353 | 7647 | 0.290 | 0.010 | 0.019 | 0.00292 |
| Union | 31847 | 10798 | 0.339 | 0.014 | 0.026 | 0.00517 |
| Rank_rp | 31824 | 10677 | 0.336 | 0.014 | 0.026 | 0.00512 |
| Rank_av | 31546 | 10280 | 0.326 | 0.013 | 0.025 | 0.00461 |
| Set of 1307 direct regulatory interactions | ||||||
| LeMoNe_qopt25R | 561 | 26 | 0.046 | 0.020 | 0.028 | 0.00128 |
| LemoNe_qopt50R | 452 | 12 | 0.027 | 0.009 | 0.014 | 0.00032 |
| ClrR | 727 | 51 | 0.070 | 0.039 | 0.050 | 0.00537 |
| TwixTrixR | 311 | 16 | 0.051 | 0.012 | 0.020 | 0.00065 |
| Union | 569 | 40 | 0.070 | 0.031 | 0.043 | 0.00259 |
| Rank_rp | 574 | 40 | 0.070 | 0.031 | 0.043 | 0.00248 |
| Rank_av | 575 | 45 | 0.078 | 0.034 | 0.048 | 0.00272 |
The table shows the individual reverse-engineering methods LeMoNe_qopt25R, LemoNe_qopt50R, ClrR, and TwixTrixR, as well as their ensemble solutions by union, mean reciprocal rank (Rank_rp), and average rank (Rank_av) aggregation (i.e., the abiotic stress GRN; underlined), against three reference sets: (1) an assembled interaction set of 52,328 experimental protein-DNA and regulatory interactions, (2) an extended set of (1) containing all indirect hidden paths of 789,068 interactions (paths of length greater than one), and (3) a confined set of (1) containing only 1307 direct regulatory interactions. Npred = number of predictions made for TFs and target genes belonging to the reference set; TP = number of true positives; Precision = TP/Npred; Recall = TP/number of interactions in the reference set; F = 2 × precision × recall/(precision + recall) = 2 × TP/(2 × TP + FP + FN); AUPR = estimated AUPR curve. Due to the size of the reference sets, the AUPR calculation of the confined set is based on only a few hundreds of points, while for the other two reference sets, this is thousands to tens of thousands of points.