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Bioinformatics Advance Access published online on December 1, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl616
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 31, 2006
Revised November 8, 2006
Accepted November 28, 2006

Article

RelEx - relation extraction using dependency parse trees

Katrin Fundel 1 *, Robert Küffner 1, and Ralf Zimmer 1

1 Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333 München, Germany

* To whom correspondence should be addressed.
Katrin Fundel, E-mail: katrin.fundel{at}bio.ifi.lmu.de


  Abstract

Motivation: The discovery of regulatory pathways, signal cascades, metabolic processes or disease models requires knowledge on individual relations like e.g. physical or regulatory interactions between genes and proteins. Most interactions mentioned in the free text of biomedical publications are not yet contained in structured databases.

Results: We developed RelEx, an approach for relation extraction from free text. It expands on natural language preprocessing by applying a small number of simple rules to achieve competitive recall and precision. We applied RelEx on a comprehensive set of one million MEDLINE abstracts dealing with relations of proteins and extracted approx. 150.000 relations.

Availability: The used natural language preprocessing tools are free for use for academic research. Test sets and relation term lists are available from our web-site (http://www.bio.ifi.lmu.de/publications/RelEx/).

Associate Editor: Satoru Miyano
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