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

Table 9 Differences in number of pathways and AUC of pathway centrality in three different pathway databases

Database	Avg # pathways per drug target	Avg # pathways per non-drug target	Max # pathways per drug target	Max # pathways per non-drug target	AUC – Number of pathways for proteins in one pathway or more	AUC – Number of pathways for proteins in zero pathways or more
PID	4.13	2.32	44	30	0.59	0.60
Reactome	1.85	1.71	17	23	0.53	0.81
KEGG	3.99	2.74	51	51	0.62	0.83

Drug targets are, on average, crossed by more pathways than non-drug targets. However, these values are relative to each pathway database.
KEGG allows the best performance for pathway centrality when using only the data in its database, while Reactome performs poorly. However, including the UniProt proteins not present in each database as part of the analysis, leads to an increase in the performance, and having both KEGG and Reactome as data sources, and the pathway centrality as predictor, can be considered as the best prediction platform investigated here.

ISSN: 1471-2105