Algorithm for predicting weighted protein complexes by using modularity function

Abstract

Due to the difficulties in obtaining the identification results of protein complexes with higher accuracy, as well as the running efficiency of the protein complexes identification algorithm based on weighted modularity function clustering is low, a novel protein complex recognition algorithm named IWPC-MF (Algorithm for identifying weighted protein complexes based on modularity function) was proposed. Firstly, we build the TCEWS (topological clustering for weighted edges) algorithm, which is founded on improved edge clustering coefficient and the combination of Pearson correlation coefficient with edge point clustering coefficient. Secondly, seed nodes were selected according to the weight of nodes, then the IMS (initial module selection strategy) program has been proposed. The similarity measurement and the protein attachment degree between nodes were selected to obtain the initial clustering module by traversing neighbors of seeds. Finally, based on the tightness, we take the IMC (initial module combination strategy) module with modularity function was added to initial module and finally complete the protein complex detection, which can overcome the defect that the traditional modularity function cannot identify the overlapped and small complex. IWPC-MF algorithm was used to identify protein complexes on DIP (the Database of Interacting Proteins) data, in order to reduce effectively the computational complexity and randomness, speed up the clustering speed. The experimental results show that IWPC-MF algorithm has better performance on accuracy and recall, which is more reasonable to identify protein complexes.