Table 5.
Drug target predictive power of degree and centralities for different reliable subsets
| Network | Number of proteins in network | AUC – Degree | AUC - BC | AUC - CC |
|---|---|---|---|---|
| Full PIN, spoke |
16078 |
0.6139 |
0.6294 |
0.5795 |
| B subset |
14408 |
0.6114 |
0.6171 |
0.5764 |
| Non-predicted interactions |
14928 |
0.5916 |
0.6128 |
0.5647 |
| LTP subset |
10591 |
0.5794 |
0.6066 |
0.5482 |
| BioGRID only |
8642 |
0.5082 |
0.5467 |
0.4874 |
| MI score, IntAct > 0.6 |
219 |
0.6353 |
0.5347 |
0.4382 |
| MI score, PSICQUIC > 0.7 |
747 |
0.5719 |
0.5725 |
0.5414 |
| Rual+Stelzl only | 3575 | 0.5004 | 0.5045 | 0.5011 |
The AUC was evaluated for degree and centrality ranks of the full PIN, five reliable subsets and two small subsets used in the literature. The best degree performance is achieved by the MI-IntAct score greater than 0.6; however, this subset contains 219 proteins only, making it of limited applicability. The second best performance is achieved by the full PIN and the B subset. Other reliable subsets (non-predicted, PSICQUIC, LTP) have a slightly inferior performance, while BioGRID and Rual+Stelzl perform close to randomness.
The best centrality performance is achieved by the full PIN, followed by three reliable subsets (B, non-predicted and LTP). Both MI-scores and both limited data sets perform close to randomness.