1 A hybrid approach to extract protein-protein interactions

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 auto-matically categorize the data into subsets based on its semantic properties and extract candidate PPI pairs from these subsets. Second, we apply support vector machines (SVM) to classify candi-date 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 to 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 compu-tational efficiency. Availability: The source code and scripts used in this paper are available for academic use a