Skip Navigation



Bioinformatics Advance Access published online on November 15, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti783
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/6/658    most recent
bti783v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ruch, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ruch, P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 18, 2005
Revised November 11, 2005
Accepted November 13, 2005

Article

Automatic assignment of biomedical categories: toward a generic approach

Patrick Ruch 1 *

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

* To whom correspondence should be addressed.
Patrick Ruch, E-mail: Patrick.Ruch{at}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 less than 20% for GO, establishing a new baseline for categorizers based on retrieval methods.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 J. Am. Med. Inform. Assoc.Home page
S. Sohn, W. Kim, D. C. Comeau, and W. J. Wilbur
Optimal Training Sets for Bayesian Prediction of MeSH(R) Assignment
J. Am. Med. Inform. Assoc., July 1, 2008; 15(4): 546 - 553.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.