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Extracting protein-protein interactions from MEDLINE using the Hidden Vector State model

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2008). Extracting protein-protein interactions from MEDLINE using the Hidden Vector State model. International Journal of Bioinformatics Research and Applications, 4(1) pp. 64–80.

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DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1504/IJBRA.2008.017164
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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.

Item Type: Journal Article
Copyright Holders: 2008 Inderscience Enterprises Ltd.
ISSN: 1744-5485
Keywords: information extraction; Hidden Vector State model; protein-protein Interactions; PPIs; bioinformatics
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23794
Depositing User: Kay Dave
Date Deposited: 11 Nov 2010 12:30
Last Modified: 26 Oct 2012 04:33
URI: http://oro.open.ac.uk/id/eprint/23794
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