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.

Publication DOI: https://doi.org/10.1504/IJBRA.2008.017164
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: International journal of bioinformatics research and applications (4, 2008) http://www.inderscience.com/offer.php?id=17164 © Inderscience Enterprises Ltd.
Uncontrolled Keywords: information extraction,hidden vector state model,protein-protein interactions extraction
Full Text Link: http://www.inde ... er.php?id=17164
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2008-02
Authors: Zhou, Deyu
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Kwoh, Chee K.

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