Protein Complex Identification Method based on Weighted Subgraph Clustering and Biocompatibility Evaluation

In this paper, we proposed a Weighted Subgraph Clustering for Protein Complex Identification (WSC-PCI) method , which combined with Gene Ontology (GO) annotations and improved Edge Clustering Coefficient (ECC) to indentify the protein complex over weighted Protein- Protein Interaction (PPI) network. By using the comprehensive weighted method to calculate the node weights within the PPI network, in order to mine the valuable and core subgraphs, which are related to the biologically significant and real protein complexes based on the core-attachment structure requirements. And then, calculating Biocompatibility value (BA-value) to identify potential proteins, which have more stable and good biocompatibility. By comparing with multiple algorithms on real datasets of different species, the results show that WSC-PCI outperforms other methods with regard to recall, precision and MMR, and the authenticity and biological significance of the identification results are verified by p-value analysis.