Correlating interactions with gene expressions to detect protein complexes in protein interaction networks

In protein-protein interaction networks, proteins combine into macromolecular complexes to execute essential functions in the cells, such as replication, transcription, protein transport. Considering the certain rate of false positive and false negative interactions, we take a confidence probability on interactions and correlate interactions and gene expression data to assign weights to edges in PPI networks. Then we propose the CIGE algorithm to detecting protein complexes from protein interaction networks. Our algorithm takes a maximal full-connected sub-graph as core graph of a seed node, and decides whether a node belongs to a protein complex through judging in-module weight and out-module weight between core graph and nodes out of core graph. Experiment results show that our algorithm has an excellent performance in both accuracy and hit rate.