Extracting protein-protein interactions from MEDLINE using the hidden vector state model

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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.

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  • Extracting protein-protein interactions

    Rights statement: International journal of bioinformatics research and applications (4, 2008) http://www.inderscience.com/offer.php?id=17164 © Inderscience Enterprises Ltd.

    Accepted author manuscript, 211 KB, PDF-document

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Original languageEnglish
Pages (from-to)64-80
Number of pages17
JournalInternational Journal of Bioinformatics Research and Applications
Volume4
Issue1
DOIs
StatePublished - Feb 2008

Bibliographic note

International journal of bioinformatics research and applications (4, 2008) http://www.inderscience.com/offer.php?id=17164 © Inderscience Enterprises Ltd.

    Keywords

  • information extraction, hidden vector state model, protein-protein interactions extraction

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