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Bioinformatics Advance Access originally published online on November 15, 2005
Bioinformatics 2006 22(6):658-664; doi:10.1093/bioinformatics/bti783
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Automatic assignment of biomedical categories: toward a generic approach

Patrick Ruch *

University Hospitals of Geneva, Medical Informatics Service CH-1201, Geneva

*To whom correspondence should be addressed.

ABSTRACT

Motivation: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent.

Methods: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units.

Results and Conclusion: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.

Contact: Patrick.Ruch{at}sim.hcuge.ch

Received on April 18, 2005; revised on November 11, 2005; accepted on November 13, 2005

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