An efficient protein complex mining algorithm based on multistage kernel extension

Inspired by the formation process of cliques of the complex social network and the centrality-lethality rule, and integrating the idea of critical proteins recognition in the Protein-Protein Interaction (PPI) network, we propose a new protein complex mining algorithm called MKE (Multistage Kernel Extension). MKE first recognizes the nodes with high degree as the first level kernel of protein complex, then adds the weighted best neighbor node of the first level kernel into the current kernel to form the second level kernel of the protein complex, this process is repeated, extending the current kernel to form protein complex. Overlapped protein complexes are merged to form the final protein complex set. The results show that MKE has better accuracy compared with the classical clique percolation method. MKE also performs better than markov clustering algorithm on Gene Ontology semantic similarity and co-localization enrichment and can effectively identify protein complexes with biological significance.