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Bioinformatics Advance Access originally published online on August 12, 2007
Bioinformatics 2007 23(20):2768-2774; doi:10.1093/bioinformatics/btm393
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© 2007 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.

Learning string similarity measures for gene/protein name dictionary look-up using logistic regression

Yoshimasa Tsuruoka 1,*, John McNaught 1,2, Jun'i;chi Tsujii 1,2,3 and Sophia Ananiadou 1,2

1School of Computer Science, The University of Manchester, Manchester, 2National Centre for Text Mining (NaCTeM), Manchester, UK and 3Department of Computer Science, The University of Tokyo, Japan

*To whom correspondence should be addressed.


  Abstract

Motivation: One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e. ID or accession number in the database, due to seemingly small differences of names. Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names. However, the accuracy of soft matching highly depends on the similarity measure employed.

Results: We used logistic regression for learning a string similarity measure from a dictionary. Experiments using several large-scale gene/protein name dictionaries showed that the logistic regression-based similarity measure outperforms existing similarity measures in dictionary look-up tasks.

Availability: A dictionary look-up system using the similarity measures described in this article is available at http://text0.mib.man.ac.uk/software/mldic/

Contact: yoshimasa.tsuruoka{at}manchester.ac.uk

Associate Editor: Jonathan Wren

Received on June 3, 2007; revised on July 27, 2007; accepted on July 28, 2007

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