Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

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.

ISSN: 1471-2105