In order to overcome the limitations of global modularity and the deficiency of local modularity, we propose a hybrid modularity measure Local-Global Quantification LGQ which considers global modularity and local modularity together. LGQ adopts a suitable module feature adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interactions PPI Network. Furthermore, we develop a new protein complex mining algorithm called Best Neighbour and Local-Global Quantification BN-LGQ which integrates the best neighbour node and modularity increment. BN-LGQ expands the protein complex by fast searching the best neighbour node of the current cluster and by calculating the modularity increment as a metric to determine whether the best neighbour node can join the current cluster. The experimental results show BN-LGQ performs a better accuracy on predicting protein complexes and has a higher match with the reference protein complexes than MCL and MCODE algorithms. Moreover, BN-LGQ can effectively discover protein complexes with better biological significance in the PPI network.
['Xianjun Shen', 'Yanli Zhao', 'Yanan Li', 'Yang Yi', 'Tingting He', 'Jincai Yang']
An integrated approach to identify protein complex based on best neighbour and modularity increment