A cell-core-attachment approach for identifying protein complexes in PPI network

With the increasing of available protein-protein interaction (PPI) data can be applied to computational methods to predict protein complexes. Most methods identify dense regions, which correspond to a cluster in the PPI network, as protein complexes. However, these dense regions could be protein complexes or sets of proteins that have circumstantial close interaction, which could affect the prediction accuracy. In this paper, a new algorithm for predicting protein complexes, applied the framework of the cell-core-attachment, is proposed. The triangular structures (cells) are obtained as the center of complex cores using the edge-clustering coefficient. Then the cells are expanded to protein complex cores. Finally, the attachments are appended to their corresponding cores to form the whole protein complexes. We apply the new approach into several yeast protein interaction data. Results show that the predicted protein complexes can match with the benchmark protein complexes better than the classic algorithms. Moreover, most of these protein complexes detected by our algorithm are biologically significant.