A hybrid approach to extract protein-protein interactions from text

Abstract

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 (SVM) 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 to the state-of-the-art PPI extraction systems. Furthermore, our sys-tem achieves the best performance on cross-corpora evaluation and is superior to other approaches in terms of computational efficiency.