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论文类型：期刊论文

发表时间：2013-12-27

发表刊物：PLOS ONE

收录刊物：SCIE、PubMed、Scopus

卷号：8

期号：12

页面范围：e83841

ISSN号：1932-6203

摘要：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.