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Zhou, Deyu; He, Yulan and Kwoh, Chee Keong
(2008).
DOI: https://doi.org/10.1504/IJBRA.2008.017164
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.
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About
- Item ORO ID
- 23794
- Item Type
- Journal Item
- ISSN
- 1744-5485
- Keywords
- information extraction; Hidden Vector State model; protein-protein Interactions; PPIs; bioinformatics
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Research Group
- Centre for Research in Computing (CRC)
- Copyright Holders
- © 2008 Inderscience Enterprises Ltd.
- Depositing User
- Kay Dave