Biclique extension as an effective approach to identify missing links in metabolic compound-protein interaction networks.

Motivation: Metabolic networks are complex systems of chemical reactions proceeding via physical interactions between metabolites and proteins. We aimed to predict previously unknown compound-protein interactions (CPI) in metabolic networks by applying biclique extension, a network-structure-based prediction method.

Results: We developed a workflow, named BiPredict, to predict CPIs based on biclique extension and applied it to and human using their respective known CPI networks as input.