title: Extracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State model.
abstract: A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.
authors.affiliation: Informatics Research Centre, University of Reading, 3rd Floor, Philip Lyle Building, Whiteknights, Reading RG6 6BX, UK. d.zhou@reading.ac.uk
authors.forename: Deyu
authors.initials: D
authors.lastname: Zhou
authors.forename: Yulan
authors.initials: Y
authors.lastname: He
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
mesh_terms: D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D025941:Protein Interaction Mapping / Q000379:methods*; D014481:United States; D018875:Vocabulary, Controlled