BACKGROUND: Although many genomic features have been used in the prediction of 
protein-protein interactions (PPIs), frequently only one is used in a 
computational method. After realizing the limited power in the prediction using 
only one genomic feature, investigators are now moving toward integration. So 
far, there have been few integration studies for PPI prediction; one failed to 
yield appreciable improvement of prediction and the others did not conduct 
performance comparison. It remains unclear whether an integration of multiple 
genomic features can improve the PPI prediction and, if it can, how to integrate 
these features.
RESULTS: In this study, we first performed a systematic evaluation on the PPI 
prediction in Escherichia coli (E. coli) by four genomic context based methods: 
the phylogenetic profile method, the gene cluster method, the gene fusion 
method, and the gene neighbor method. The number of predicted PPIs and the 
average degree in the predicted PPI networks varied greatly among the four 
methods. Further, no method outperformed the others when we tested using three 
well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based 
on these comparisons, we developed a novel integrated method, named InPrePPI. 
InPrePPI first normalizes the AC value (an integrated value of the accuracy and 
coverage) of each method using three positive datasets, then calculates a weight 
for each method, and finally uses the weight to calculate an integrated score 
for each protein pair predicted by the four genomic context based methods. We 
demonstrate that InPrePPI outperforms each of the four individual methods and, 
in general, the other two existing integrated methods: the joint observation 
method and the integrated prediction method in STRING. These four methods and 
InPrePPI are implemented in a user-friendly web interface.
CONCLUSION: This study evaluated the PPI prediction by four genomic context 
based methods, and presents an integrated evaluation method that shows better 
performance in E. coli.