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Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions

Overview of attention for article published in Journal of Cheminformatics, May 2013
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Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
69 Mendeley
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Title
Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions
Published in
Journal of Cheminformatics, May 2013
DOI 10.1186/1758-2946-5-27
Authors

Faizan Sahigara, Davide Ballabio, Roberto Todeschini, Viviana Consonni

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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
Brazil 2 3%
Malaysia 1 1%
India 1 1%
Mexico 1 1%
United States 1 1%
Unknown 61 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 26%
Researcher 13 19%
Student > Master 11 16%
Student > Doctoral Student 6 9%
Other 5 7%
Other 10 14%
Unknown 6 9%
Readers by discipline Count As %
Chemistry 29 42%
Computer Science 10 14%
Agricultural and Biological Sciences 7 10%
Engineering 6 9%
Mathematics 3 4%
Other 6 9%
Unknown 8 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 May 2013.
All research outputs
#3,160,100
of 4,505,402 outputs
Outputs from Journal of Cheminformatics
#203
of 253 outputs
Outputs of similar age
#63,882
of 89,863 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 14 outputs
Altmetric has tracked 4,505,402 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 253 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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 89,863 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.