A novel proteins complex identification based on connected affinity and multi-level seed extension

The identification of modules in complex networks is important for the understanding of systems. Recent studies have shown those functional modules can be identified from the protein interaction a network, what's more, the complex modules have not only relatively high density, but also have high coefficient of affinity. However, these analyses are challenging because of the presence of unreliable interactions in PPT network. In this paper, in order to mine overlapping functional modules with various and effective biological characteristics, we propose a novel algorithm based on Connected Affinity and Multi-level Seed Extension (CAMSE). First, CAMSE integrates protein-protein interactions (PPI) with the protein-protein Connected Coefficient (CC) inferred from protein complexes collected in the MIPS database to enhance the modularization and biological character of the interaction network. Then we complete the seed selection, inner kernel extensions and outer extension to get core candidate function modules step by step. Finally, we integrated the modules with high repeat rate. The experimental results show that CAMSE can detect the functional modules much more effectively and accurately when it compared with other state-of-art algorithms CPM, CACE and IPC-MCE.