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Bioinformatics. 2006 Mar 15;22(6):658-64. Epub 2005 Nov 15.

Automatic assignment of biomedical categories: toward a generic approach.

Author information

  • University Hospitals of Geneva, Medical Informatics Service CH-1201, Geneva. Patrick.Ruch@sim.hcuge.ch

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.

PMID:
16287934
[PubMed - indexed for MEDLINE]
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