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Bioinformatics Advance Access originally published online on September 16, 2009
Bioinformatics 2009 25(22):2983-2991; doi:10.1093/bioinformatics/btp535
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A dictionary to identify small molecules and drugs in free text

Kristina M. Hettne 1,2,3,*, Rob H. Stierum 3,4, Martijn J. Schuemie 2, Peter J. M. Hendriksen 5, Bob J. A. Schijvenaars 6, Erik M. van Mulligen 2, Jos Kleinjans 1,3 and Jan A. Kors 2

1Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, 2Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3Department of Toxicoinformatics, Netherlands Toxicogenomics Centre, Maastricht, 4Business unit Biosciences, Physiological Genomics, TNO Quality of Life, Zeist, 5Safety and Health, RIKILT Institute of Food Safety, Wageningen, The Netherlands and 6Collexis Holdings Inc., Columbia SC, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers.

Results: We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (i) each of the different processing steps increase precision with a minor loss of recall; (ii) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (iii) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (iv) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary.

Availability: The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web site http://www.biosemantics.org/chemlist.

Contact: k.hettne{at}erasmusmc.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on May 13, 2009; revised on September 3, 2009; accepted on September 7, 2009

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