Protein functional module detection based on closely associated degree

Density modularity can overcome this defect, but it use Simulated Annealing (SA) algorithm to search the maximal density modularity, which can´t ensure to rapidly search the global optimal solution of problem. Based on this consideration, we propose a Closely Associated Degree (CAD) algorithm to discover protein functional module which continuously improve density modularity of PPI network. CAD first analyze the associated degree of protein node, then join it into the maximal associated degree module. When all modular structure are stable, CAD merges the pair of module that can bring the maximum increment of density modularity. This process is continually repeated that make density modularity to grow rapidly. Experimental results show that the CAD algorithm can effectively and accurately identify protein functional modules with biological significance in large-scale PPI network.