Identification of protein complexes by integrating multiple alignment of protein interaction networks
Author(s) -
Cheng-Yu Ma,
YiPing Phoebe Chen,
Bonnie Berger,
Chung-Shou Liao
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx043
Subject(s) - identification (biology) , computer science , cluster analysis , interaction network , protein–protein interaction , protein interaction networks , computational biology , complex network , systems biology , data mining , biology , artificial intelligence , genetics , gene , botany , world wide web
Protein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes.
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