PPI-IRO: a two-stage method for protein-protein interaction extraction based on interaction relation ontology PPI-IRO: a two-stage method for protein-protein interaction extraction 101

Abstract: Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and BioInfer, respectively, which are superior to most existing extraction methods. 100 Chuan-Xi Li et al. Keywords: protein-protein interaction; interaction relation ontology; relation word; sentence typed dependency; relation extraction; text mining; information extraction; bioinformatics. Reference to this paper should be made as follows: Li, C-X., Chen, P., Wang, R-J., Wang, X-J., Su, Y-R. and Li, J. • computational discovery of non-coding RNA genes and their regulatory mechanisms • identifi cation of biological roles of non-coding RNAs in eukaryotic gene expression regulatory networks. She has published more than 40 journal papers. These journals include: Nature, J