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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, May 2010
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
169 Dimensions

Readers on

mendeley
208 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, May 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 5 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 208 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 2%
Netherlands 3 1%
Germany 3 1%
Canada 2 <1%
Korea, Republic of 1 <1%
Italy 1 <1%
Portugal 1 <1%
India 1 <1%
Other 6 3%
Unknown 179 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 26%
Researcher 51 25%
Student > Master 26 13%
Professor > Associate Professor 16 8%
Other 13 6%
Other 33 16%
Unknown 14 7%
Readers by discipline Count As %
Computer Science 68 33%
Agricultural and Biological Sciences 48 23%
Chemistry 27 13%
Biochemistry, Genetics and Molecular Biology 12 6%
Pharmacology, Toxicology and Pharmaceutical Science 9 4%
Other 22 11%
Unknown 22 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 14 June 2022.
All research outputs
#6,445,739
of 21,775,893 outputs
Outputs from BMC Bioinformatics
#2,501
of 7,036 outputs
Outputs of similar age
#42,267
of 143,890 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 19 outputs
Altmetric has tracked 21,775,893 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,036 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 63% 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 143,890 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 19 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 73% of its contemporaries.