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On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Overview of attention for article published in Journal of Cheminformatics, April 2014
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1 tweeter

Citations

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9 Dimensions

Readers on

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23 Mendeley
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Title
On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?
Published in
Journal of Cheminformatics, April 2014
DOI 10.1186/1758-2946-6-11
Pubmed ID
Authors

Rajarshi Guha, José L Medina-Franco

Abstract

Most work on the topic of activity landscapes has focused on their quantitative description and visual representation, with the aim of aiding navigation of SAR. Recent developments have addressed applications such as quantifying the proportion of activity cliffs, investigating the predictive abilities of activity landscape methods and so on. However, all these publications have worked under the assumption that the activity landscape models are "real" (i.e., statistically significant).

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

Geographical breakdown

Country Count As %
Mexico 1 4%
Bulgaria 1 4%
Germany 1 4%
Romania 1 4%
Unknown 19 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 30%
Student > Ph. D. Student 5 22%
Professor > Associate Professor 3 13%
Student > Doctoral Student 3 13%
Other 1 4%
Other 2 9%
Unknown 2 9%
Readers by discipline Count As %
Chemistry 8 35%
Computer Science 6 26%
Agricultural and Biological Sciences 2 9%
Medicine and Dentistry 2 9%
Mathematics 1 4%
Other 1 4%
Unknown 3 13%

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 02 April 2014.
All research outputs
#4,606,229
of 6,228,364 outputs
Outputs from Journal of Cheminformatics
#275
of 315 outputs
Outputs of similar age
#88,970
of 133,107 outputs
Outputs of similar age from Journal of Cheminformatics
#19
of 21 outputs
Altmetric has tracked 6,228,364 research outputs across all sources so far. This one is in the 15th percentile – i.e., 15% of other outputs scored the same or lower than it.
So far Altmetric has tracked 315 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 1st percentile – i.e., 1% 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 133,107 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.