Detecting Complexes From Edge-Weighted PPI Networks Via Genes Expression Analysis

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signaling and biological processes in a cell.Lots of proteins are important roles of life activity,after binding as complexes.An important challenge is to systematically analyze and fully understand how proteins interact to complete life activities.This paper analyzes the characteristics of protein network from topological structure,and then explores the function of protein complex and function module and annotated unknown protein,which is becoming the important focus of research at home and abroad.Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization.We propose a novel method to identify complexes on PPI networks,based on differential co-expression information.This paper proposes a detection algorithm of two stages,first on a wide and full of initial clustering,involving multiple potential complexes as widely as possible,we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks.Then,we propose some significant features,such as graph information and gene expression,to filter and modify complexes predicted by Markov Cluster Algorithm.To evaluate our method,we test on two experimental yeast PPI networks.Comparing to existing methods,our method has achieved higher precision.Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks,with the prediction quality improved in terms of many evaluation criteria.