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Bioinformatics Advance Access originally published online on April 9, 2008
Bioinformatics 2008 24(11):1410-1412; doi:10.1093/bioinformatics/btn117
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

MedEvi: Retrieving textual evidence of relations between biomedical concepts from Medline

Jung-jae Kim *, Piotr Pezik and Dietrich Rebholz-Schuhmann

EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

*To whom correspondence should be addressed.


  Abstract

Summary: Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results.

Availability: http://www.ebi.ac.uk/tc-test/textmining/medevi/

Contact: kim{at}ebi.ac.uk

Associate Editor: Alfonso Valencia

Received on November 8, 2007; revised on January 18, 2008; accepted on March 31, 2008

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