Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network

EasyChair Preprint no. 1211

Abstract

Protein complexes are aggregates of protein molecules that play important roles in biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function.Identifying complexes from raw protein protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance.We introduce a novel method called CFM to detect protein complexes.Experiments were carried out on the PPI datasets of DIP, Krogan, HPRD and Mouse respectively. MIPS and PCDq were used as standard complexes of Saccharomyces cerevisiae and human.The results show that compared with the six classical PPI algorithms of WCOACH, ClusterONE, SPICi, MCL, MCODE and CFinder,The algorithm is similar to the classical PPI clustering algorithm in Saccharomyces cerevisiae, and the accuracy of protein complex prediction in human PPI network is higher than the other six algorithms, and the number of protein complexes predicted in the mouse PPI network is higher than other algorithms, which improves the accuracy of predicting protein complexes on multi-species PPI networks.In addition, D3 visualization technology was applied to PPI network visualization field, which provided a beneficial reference for the mining and analysis of biological network modules.

Keyphrases: Clustering, protein complex, protein-protein interaction network

@Booklet{EasyChair:1211,
  author = {Dongwen Zhang and Peiheng Wang and Yunfeng Xu},
  title = {Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network},
  howpublished = {EasyChair Preprint no. 1211},

  year = {EasyChair, 2019}}