Clique Based Approach to Predict Complexes from Protein Interaction Network

The ample availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry research efforts to understand cells' organization, processes, functioning by analyzing these at the network level. In bioinformatics mining fields, clustering requires lot attraction discover clusters interacting proteins. Clustering proteins in PPI has been an excellent method for discovering functional modules, disclosing functions unknown proteins, other tasks numerous over last decade. this research, unique graph approach is proposed detect dense neighborhoods (highly connected regions) graph, including protein complexes. Our technique first finds size-3 cliques then expands based on their affinity produce maximal regions. To highlight efficiency our suggested strategy, we present experimental results using yeast human data. predicted complexes match or overlap much better with gold standard CYC-2008 CORUM benchmark databases than existing approaches.