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论文类型：期刊论文

发表时间：2013-11-07

发表刊物：PROTEOME SCIENCE

收录刊物：SCIE

卷号：11

ISSN号：1477-5956

摘要：Background: Many computational approaches have been developed to detect protein complexes from protein-protein interaction (PPI) networks. However, these PPI networks are always built from high-throughput experiments. The presence of unreliable interactions in PPI network makes this task very challenging.
   Methods: In this study, we proposed a Genetic-Algorithm Fuzzy Naive Bayes (GAFNB) filter to classify the protein complexes from candidate subgraphs. It takes unreliability into consideration and tackles the presence of unreliable interactions in protein complex. We first got candidate protein complexes through existed popular methods. Each candidate protein complex is represented by 29 graph features and 266 biological property based features. GAFNB model is then applied to classify the candidate complexes into positive or negative.
   Results: Our evaluation indicates that the protein complex identification algorithms using the GAFNB model filtering outperform original ones. For evaluation of GAFNB model, we also compared the performance of GAFNB with Naive Bayes (NB). Results show that GAFNB performed better than NB. It indicates that a fuzzy model is more suitable when unreliability is present.
   Conclusions: We conclude that filtering candidate protein complexes with GAFNB model can improve the effectiveness of protein complex identification. It is necessary to consider the unreliability in this task.