MOTIVATION: Protein-protein interactions (PPIs) play an important role in 
understanding biological processes. Although recent research in text mining has 
achieved a significant progress in automatic PPI extraction from literature, 
performance of existing systems still needs to be improved.
RESULTS: In this study, we propose a novel algorithm for extracting PPIs from 
literature which consists of two phases. First, we automatically categorize the 
data into subsets based on its semantic properties and extract candidate PPI 
pairs from these subsets. Second, we apply support vector machines (SVMs) to 
classify candidate PPI pairs using features specific for each subset. We obtain 
promising results on five benchmark datasets: AIMed, BioInfer, HPRD50, IEPA and 
LLL with F-scores ranging from 60% to 84%, which are comparable with the 
state-of-the-art PPI extraction systems. Furthermore, our system achieves the 
best performance on cross-corpora evaluation and comparative performance in 
terms of computational efficiency.
AVAILABILITY: The source code and scripts used in this article are available for 
academic use at http://staff.science.uva.nl/~bui/PPIs.zip
CONTACT: bqchinh@gmail.com.