Data integration and supervised learning based protein complex detection method

The rapidly growing biomedical literature provides a significantly large and readily available source of PPI data. In this paper, we present supervised learning and data integration based complex detection approach. In this approach, a sophisticated natural language processing system, PPIExtractor, is employed to extract new PPI interactions from biomedical literature which are then integrated into original PPI networks. Then a supervised learning model, built by via of the information of available known complexes, is used in the multiple complex detection stages, e.g. the cliques filtering, growth, and candidate complex filtering. The experimental results on three yeast PPI networks demonstrate the effectiveness of our approach.