Extracting biological events from text using simple syntactic patterns
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Mixture of logistic models and an ensemble approach for protein-protein interaction extraction
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Tree kernel-based protein-protein interaction extraction from biomedical literature
Journal of Biomedical Informatics
Combining tree structures, flat features and patterns for biomedical relation extraction
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
High precision rule based PPI extraction and per-pair basis performance evaluation
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
Learning bayesian network using parse trees for extraction of protein-protein interaction
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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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