Abstract
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.