Nucleic Acids Research, 2005, Vol. 33, Database issue D289-D293
© 2005, the authors
Nucleic Acids Research, Vol. 33, Database issue © Oxford University Press 2005; all rights reserved
Biomedical term mapping databases
Advanced Center for Genome Technology, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd, Rm 2025, Norman, OK 73019, USA, 1 Department of Molecular Genetics and Microbiology, Duke University Medical Center Computational and Applied Genomics Program, Duke Institute for Genome Sciences and Policy, Durham, NC 237710-0001, USA, 2 Department of Computer Science, Brandeis University, Waltham, MA 02454-9110, USA, 3 Information Dynamics Lab, Hewlett-Packard Laboratories, Palo Alto, CA 94304, USA, 4 McDermott Center for Human Growth and Development and the Center for Biomedical Inventions, University of Texas Southwestern Medical Center, TX 75390, USA and 5 Department of Genetics, Stanford University School of Medicine, CA 94305-5120, USA
* To whom correspondence should be addressed. Tel: +1 405 325 3415; Fax: +1 405 325 3442; Email: Jonathan.Wren{at}OU.edu
Received August 15, 2004; Revised and Accepted October 29, 2004
Longer words and phrases are frequently mapped onto a shorter form such as abbreviations or acronyms for efficiency of communication. These abbreviations are pervasive in all aspects of biology and medicine and as the amount of biomedical literature grows, so does the number of abbreviations and the average number of definitions per abbreviation. Even more confusing, different authors will often abbreviate the same word/phrase differently. This ambiguity impedes our ability to retrieve information, integrate databases and mine textual databases for content. Efforts to standardize nomenclature, especially those doing so retrospectively, need to be aware of different abbreviatory mappings and spelling variations. To address this problem, there have been several efforts to develop computer algorithms to identify the mapping of terms between short and long form within a large body of literature. To date, four such algorithms have been applied to create online databases that comprehensively map biomedical terms and abbreviations within MEDLINE: ARGH (http://lethargy.swmed.edu/ARGH/argh.asp), the Stanford Biomedical Abbreviation Server (http://bionlp.stanford.edu/abbreviation/), AcroMed (http://medstract.med.tufts.edu/acro1.1/index.htm) and SaRAD (http://www.hpl.hp.com/research/idl/projects/abbrev.html). In addition to serving as useful computational tools, these databases serve as valuable references that help biologists keep up with an ever-expanding vocabulary of terms.
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