Title |
The BioLexicon: a large-scale terminological resource for biomedical text mining
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Published in |
BMC Bioinformatics, October 2011
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DOI | 10.1186/1471-2105-12-397 |
Pubmed ID | |
Authors |
Paul Thompson, John McNaught, Simonetta Montemagni, Nicoletta Calzolari, Riccardo del Gratta, Vivian Lee, Simone Marchi, Monica Monachini, Piotr Pezik, Valeria Quochi, CJ Rupp, Yutaka Sasaki, Giulia Venturi, Dietrich Rebholz-Schuhmann, Sophia Ananiadou |
Abstract |
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 3 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 5 | 5% |
United States | 3 | 3% |
Spain | 2 | 2% |
Germany | 1 | <1% |
Turkey | 1 | <1% |
Australia | 1 | <1% |
France | 1 | <1% |
Switzerland | 1 | <1% |
Nigeria | 1 | <1% |
Other | 3 | 3% |
Unknown | 92 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 34 | 31% |
Student > Ph. D. Student | 18 | 16% |
Student > Postgraduate | 8 | 7% |
Student > Master | 7 | 6% |
Student > Bachelor | 5 | 5% |
Other | 25 | 23% |
Unknown | 14 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 36 | 32% |
Agricultural and Biological Sciences | 27 | 24% |
Biochemistry, Genetics and Molecular Biology | 5 | 5% |
Linguistics | 5 | 5% |
Medicine and Dentistry | 5 | 5% |
Other | 16 | 14% |
Unknown | 17 | 15% |