Syntactic Features for Protein-Protein Interaction Extraction.

Background: Extracting Protein-Protein Interactions (PPI) from research papers is a way of translating information from English to the language used by the databases that store this information. With recent advances in automatic PPI detection, it is now possible to speed up this process considerably. Syntactic features from different parsers for biomedical English text are readily available, and can be used to improve the performance of such PPI extraction systems. Results: A complete PPI system was built. It uses a deep syntactic parser to capture the semantic meaning of the sentences, and a shallow dependency parser to improve the performance further. Machine learning is used to automatically make rules to extract pairs of interacting proteins from the semantics of the sentences. The results have been evaluated using the AImed corpus, and they are better than earlier published results. The F-score of the current system is 69.5% for cross-validation between pairs that may come from the same abstract, and 52.0% when complete abstracts are hidden until final testing. Automatic 10-fold cross-validation on the entire AImed corpus can be done in less than 45 minutes on a single server. We also present some previously unpublished statistics about the AImed corpus, and a short analysis of the AImed representation language. Conclusions: We present a PPI extraction system, using different syntactic parsers to extract features for SVM with Tree Kernels, in order to automatically create rules to discover protein interactions described in the molecular biology literature. The system performance is better than other published systems, and the implementation is freely available to anyone who is interested in using the system for academic purposes. The system can help researchers quickly discover reported PPIs, and thereby increasing the speed at which databases can be populated and novel signaling pathways can be constructed.