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The BioLexicon: a large-scale terminological resource for biomedical text mining

Overview of attention for article published in BMC Bioinformatics, October 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

twitter
3 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
111 Mendeley
citeulike
16 CiteULike
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Title
The BioLexicon: a large-scale terminological resource for biomedical text mining
Published in
BMC Bioinformatics, October 2011
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

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 June 2015.
All research outputs
#5,501,719
of 22,653,392 outputs
Outputs from BMC Bioinformatics
#1,994
of 7,236 outputs
Outputs of similar age
#33,058
of 135,954 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 90 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 135,954 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.