Protein complex identification by integrating protein-protein interaction evidence from multiple sources

Bo Xu, Hongfei Lin, Yang Chen, Zhihao Yang, Hongfang Liu

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Background: Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification. Results: We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks. Conclusions: We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification.

Original languageEnglish (US)
Article numbere83841
JournalPloS one
Volume8
Issue number12
DOIs
StatePublished - Dec 27 2013

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'Protein complex identification by integrating protein-protein interaction evidence from multiple sources'. Together they form a unique fingerprint.

  • Cite this