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.
|Number of pages||17|
|Journal||International Journal of Bioinformatics Research and Applications|
|Publication status||Published - Feb 2008|
Bibliographical noteInternational journal of bioinformatics research and applications (4, 2008) http://www.inderscience.com/offer.php?id=17164
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- information extraction
- hidden vector state model
- protein-protein interactions extraction