↓ Skip to main content

PUResNet: prediction of protein-ligand binding sites using deep residual neural network

Overview of attention for article published in Journal of Cheminformatics, September 2021
Altmetric Badge

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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
35 tweeters

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
52 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Published in
Journal of Cheminformatics, September 2021
DOI 10.1186/s13321-021-00547-7
Pubmed ID
Authors

Jeevan Kandel, Hilal Tayara, Kil To Chong

Twitter Demographics

The data shown below were collected from the profiles of 35 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 15%
Researcher 7 13%
Student > Master 6 12%
Student > Bachelor 5 10%
Other 3 6%
Other 5 10%
Unknown 18 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 17%
Agricultural and Biological Sciences 5 10%
Computer Science 5 10%
Chemistry 5 10%
Pharmacology, Toxicology and Pharmaceutical Science 4 8%
Other 7 13%
Unknown 17 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 06 May 2022.
All research outputs
#1,866,767
of 22,851,489 outputs
Outputs from Journal of Cheminformatics
#173
of 836 outputs
Outputs of similar age
#44,956
of 427,112 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 28 outputs
Altmetric has tracked 22,851,489 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 836 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has done well, scoring higher than 79% 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 427,112 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 89% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.