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Learning to extract relations for protein annotation

Kim, Jee-Hyub ; Mitchell, Alex ; Attwood, Teresa K. ; Hilario, Melanie

In: Bioinformatics, 2007, vol. 23, no. 13, p. i256-i263

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    Titre
    • Learning to extract relations for protein annotation
    Auteur
    • Kim, Jee-Hyub. Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland, Faculty of Life Sciences and School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PT and European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
    • Mitchell, Alex. Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland, Faculty of Life Sciences and School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PT and European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
    • Attwood, Teresa K.. Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland, Faculty of Life Sciences and School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PT and European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
    • Hilario, Melanie. Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland, Faculty of Life Sciences and School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PT and European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
    Type de document
    • Postprint
    Identifiant OAI-PMH
    • oai:doc.rero.ch:295685
    Summary
    Motivation: Protein annotation is a task that describes protein X in terms of topic Y. Usually, this is constructed using information from the biomedical literature. Until now, most of literature-based protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever more rapidly, manual annotation becomes more difficult, and there is increasing need to automate the process. Recently, information extraction (IE) has been used to address this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real-world scenarios such as in the biomedical domain. In this article, we describe an IE system that requires only sentences labelled according to their relevance or not to a given topic by domain experts. Results: We applied our system to meet the annotation needs of a well-known protein family database; the results show that our IE system can annotate proteins with a set of extracted relations by learning relations and IE rules for disease, function and structure from only relevant and irrelevant sentences. Contact: jee.kim@cui.unige.ch