Table 8.
Performance comparison of the biclustering network using new evaluation criteria.
| Methods | EdgeC ount | TP | FP | TN | FN | FP to TP | TN to FN | AURO C | AUPR |
|---|---|---|---|---|---|---|---|---|---|
| Gold | 2194 | 2194 | 0 | 400396 | 0 | 0 | 0 | 1 | 1 |
| ALL | 5440 | 94 | 5346 | 395050 | 2100 | 4623 | 2150 | 0.7530 | 0.4614 |
| SAMBA | 1611 | 46 | 1565 | 398831 | 2148 | 1340 | 2501 | 0.6958 | 0.3644 |
| ISA | 2558 | 56 | 2502 | 397894 | 2138 | 2141 | 1151 | 0.8451 | 0.6306 |
| OPSM | 220 | 12 | 208 | 400188 | 2182 | 190 | 2453 | 0.5423 | 0.089 |
| Friedman | 947 | 22 | 925 | 399471 | 2172 | 794 | 2700 | 0.6364 | 0.2491 |
| CMSBE | 735 | 20 | 715 | 399681 | 2174 | 653 | 2750 | 0.6181 | 0.2333 |
| K-means | 380 | 13 | 367 | 400029 | 2181 | 323 | 3100 | 0.5667 | 0.1307 |
| Bivisu | 1515 | 13 | 1502 | 398894 | 2181 | 1265 | 2610 | 0.6845 | 0.3326 |
| CC | 590 | 3 | 587 | 399809 | 2191 | 507 | 2800 | 0.5943 | 0.1788 |
The performances of the various biclustering algorithms improved when false positive edges could be considered true positive edges on the basis of strong evidence in the gold network. Column 7 shows the number of false positive edges in each algorithm that could be considered true positive edges (i.e. have evidence in the gold standard network). Column 8 shows the number of true negative edges that are considered as false negative.