An integrated approach to identify protein complex based on best neighbour and modularity increment.
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
X
Shen
Yanli
Y
Zhao
Yanan
Y
Li
Yang
Y
Yi
Tingting
T
He
Jincai
J
Yang
D000465:Algorithms; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D040901:Proteomics / Q000379:methods*
D011506:Proteins