Title: Identify protein complexes based on PageRank algorithm and architecture on dynamic PPI networks
Authors: Xiujuan Lei; Jing Liang; Ling Guo
Addresses: School of Computer Science, Shaanxi Normal University, Xi'an 710119, China ' School of Computer Science, Shaanxi Normal University, Xi'an 710119, China ' College of Life Science, Shaanxi Normal University, Xi'an 710119, China
Abstract: Protein-Protein Interactions (PPI) are dynamic in cellular organisation. Protein complexes play significant roles in cells. Thus, detecting protein complexes from dynamic PPI networks is realistic. In this paper, we proposed a novel protein complexes prediction algorithm based on core-attachment structure and Pagerank algorithm (PRCA), which run in dynamic PPI networks. This method is divided into three steps. Firstly, calculating the weight value of every protein in dynamic PPI networks to obtain seed proteins. Second, considering triangular structures, cores of protein complexes are acquired. Third, calculating the PageRank value of the adjacent proteins of protein complexes, attachments of protein complexes are appended to their corresponding cores to form protein complexes. This method identifies protein complexes in dynamic PPI networks of DIP, MIPS and Krogan dataset. The experimental results show that PRCA algorithm outperforms other algorithms in precision, recall, f-measure and p-value.
Keywords: PRCA; PageRank algorithm; protein complex; PPI network; core-attachment.
International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.4, pp.350 - 364
Received: 12 Jun 2018
Accepted: 29 May 2019
Published online: 05 Aug 2019 *