Extracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State model

Authors:
Deyu Zhou;Yulan He;Chee Keong Kwoh
Affiliations:
Informatics Research Centre, University of Reading, 3rd Floor, Philip Lyle Building, Whiteknights, Reading RG6 6BX, UK.;Informatics Research Centre, University of Reading, 3rd Floor, Philip Lyle Building, Whiteknights, Reading RG6 6BX, UK.;School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, 639798 Singapore
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2008

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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.