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 Naïve 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 Naïve 
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.