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Briefings in Bioinformatics Advance Access originally published online on December 6, 2008
Briefings in Bioinformatics 2008 9(6):466-478; doi:10.1093/bib/bbn043
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© The Author 2008. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

This article appears in the following Briefings in Bioinformatics issue: Special Issue:Database Integration in Life Sciences [View the issue table of contents]

Facts from text: can text mining help to scale-up high-quality manual curation of gene products with ontologies?

Rainer Winnenburg, Thomas Wächter, Conrad Plake, Andreas Doms and Michael Schroeder

Corresponding author. Michael Schroeder, Biotechnology Center, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany. Tel: +49 351 463 40062; Fax: +49 351 463 40061; E-mail: ms{at}biotec.tu-dresden.de

The biomedical literature can be seen as a large integrated, but unstructured data repository. Extracting facts from literature and making them accessible is approached from two directions: manual curation efforts develop ontologies and vocabularies to annotate gene products based on statements in papers. Text mining aims to automatically identify entities and their relationships in text using information retrieval and natural language processing techniques. Manual curation is highly accurate but time consuming, and does not scale with the ever increasing growth of literature. Text mining as a high-throughput computational technique scales well, but is error-prone due to the complexity of natural language. How can both be married to combine scalability and accuracy? Here, we review the state-of-the-art text mining approaches that are relevant to annotation and discuss available online services analysing biomedical literature by means of text mining techniques, which could also be utilised by annotation projects. We then examine how far text mining has already been utilised in existing annotation projects and conclude how these techniques could be tightly integrated into the manual annotation process through novel authoring systems to scale-up high-quality manual curation.

Keywords: text mining, data curation, ontology generation, entity recognition, GO annotation, authoring systems

Submitted: May 23, 2008. Received (in revised form): September 10, 2008.


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