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Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

Overview of attention for article published in BMC Bioinformatics, January 2010
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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 (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
149 Dimensions

Readers on

mendeley
191 Mendeley
citeulike
14 CiteULike
connotea
1 Connotea
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Title
Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-255
Pubmed ID
Authors

Bin Chen, Xiao Dong, Dazhi Jiao, Huijun Wang, Qian Zhu, Ying Ding, David J Wild

Abstract

Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
United Kingdom 5 3%
Germany 3 2%
Netherlands 3 2%
Canada 2 1%
Sweden 1 <1%
Brazil 1 <1%
Italy 1 <1%
India 1 <1%
Other 6 3%
Unknown 162 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 26%
Researcher 49 26%
Student > Master 26 14%
Professor > Associate Professor 16 8%
Other 12 6%
Other 28 15%
Unknown 10 5%
Readers by discipline Count As %
Computer Science 63 33%
Agricultural and Biological Sciences 48 25%
Chemistry 27 14%
Medicine and Dentistry 9 5%
Pharmacology, Toxicology and Pharmaceutical Science 8 4%
Other 19 10%
Unknown 17 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 11 September 2013.
All research outputs
#2,699,314
of 11,350,565 outputs
Outputs from BMC Bioinformatics
#1,250
of 4,199 outputs
Outputs of similar age
#24,273
of 108,729 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 82 outputs
Altmetric has tracked 11,350,565 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,199 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 69% 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 108,729 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 77% of its contemporaries.
We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.