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Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Overview of attention for article published in Journal of Cheminformatics, August 2021
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)

Mentioned by

twitter
9 tweeters

Readers on

mendeley
28 Mendeley
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Title
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
Published in
Journal of Cheminformatics, August 2021
DOI 10.1186/s13321-021-00539-7
Authors

Lewis H. Mervin, Maria-Anna Trapotsi, Avid M. Afzal, Ian P. Barrett, Andreas Bender, Ola Engkvist

Twitter Demographics

The data shown below were collected from the profiles of 9 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 25%
Student > Ph. D. Student 4 14%
Student > Master 3 11%
Student > Bachelor 2 7%
Lecturer > Senior Lecturer 2 7%
Other 3 11%
Unknown 7 25%
Readers by discipline Count As %
Chemistry 6 21%
Biochemistry, Genetics and Molecular Biology 3 11%
Engineering 3 11%
Medicine and Dentistry 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 2 7%
Unknown 11 39%

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 03 September 2021.
All research outputs
#5,401,905
of 20,398,654 outputs
Outputs from Journal of Cheminformatics
#452
of 755 outputs
Outputs of similar age
#102,116
of 338,395 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 20,398,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 755 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 40th percentile – i.e., 40% 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 338,395 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 69% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them