|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|
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.
Mora and Donaldson
Mora and Donaldson BMC Bioinformatics 2012 13:294 doi:10.1186/1471-2105-13-294