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Predicting cytotoxicity from heterogeneous data sources with Bayesian learning

Overview of attention for article published in Journal of Cheminformatics, December 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
45 Mendeley
citeulike
2 CiteULike
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Title
Predicting cytotoxicity from heterogeneous data sources with Bayesian learning
Published in
Journal of Cheminformatics, December 2010
DOI 10.1186/1758-2946-2-11
Pubmed ID
Authors

Sarah R Langdon, Joanna Mulgrew, Gaia V Paolini, Willem P van Hoorn

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
India 1 2%
Austria 1 2%
United Kingdom 1 2%
Russia 1 2%
United States 1 2%
Unknown 38 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 27%
Researcher 12 27%
Other 5 11%
Student > Master 5 11%
Student > Postgraduate 3 7%
Other 5 11%
Unknown 3 7%
Readers by discipline Count As %
Chemistry 16 36%
Agricultural and Biological Sciences 11 24%
Computer Science 4 9%
Medicine and Dentistry 3 7%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 3 7%
Unknown 6 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 27 August 2015.
All research outputs
#1,815,424
of 13,131,556 outputs
Outputs from Journal of Cheminformatics
#191
of 529 outputs
Outputs of similar age
#38,256
of 233,603 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 15 outputs
Altmetric has tracked 13,131,556 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 529 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. 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 233,603 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 83% of its contemporaries.
We're also able to compare this research output to 15 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.