A self-learning graph clustering approach for protein complexes detection
ZHU Jia
WU Xing-cheng
LIN Xue-qin
XIAO Dan-yang
XIAO Jing
HUANG Jin
HE Chao-bo
Abstract：Protein complex is a group of two or more associated polypeptide chains which plays essential roles in biological process. Given a graph representing protein-protein interactions (PPI) data, it is important but non-trivial to find protein complexes, the subsets of proteins that are closely coupled, from it, particularly in the condition that the PPI network has increased greatly in capacity in the recent years. In this paper, we propose a graph based clustering approach by adopting symmetric non-negative matrix factorization, which can effectively detect densely connected subgraphs from complex networks. We compare the performance of our approach with state-of-the-art approaches in three PPI networks with a well known benchmark complexes. The experimental results show that our approach significantly outperforms other methods in three PPI networks with different data sizes and densities.
Keywords：graph clusteringprotein complexesnon-negative matrix factorization
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 776-782 )