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Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT

Overview of attention for article published in Journal of Cheminformatics, April 2020
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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 (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
1 blog
twitter
13 X users
patent
1 patent

Citations

dimensions_citation
84 Dimensions

Readers on

mendeley
175 Mendeley
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Title
Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
Published in
Journal of Cheminformatics, April 2020
DOI 10.1186/s13321-020-00430-x
Pubmed ID
Authors

Xinhao Li, Denis Fourches

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 175 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 22%
Student > Ph. D. Student 28 16%
Student > Master 21 12%
Student > Bachelor 14 8%
Other 7 4%
Other 19 11%
Unknown 48 27%
Readers by discipline Count As %
Chemistry 36 21%
Computer Science 18 10%
Biochemistry, Genetics and Molecular Biology 16 9%
Engineering 12 7%
Agricultural and Biological Sciences 7 4%
Other 29 17%
Unknown 57 33%
Attention Score in Context

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 02 December 2021.
All research outputs
#2,052,060
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#167
of 934 outputs
Outputs of similar age
#50,250
of 379,208 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 24 outputs
Altmetric has tracked 24,903,209 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 934 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 82% 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 379,208 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 86% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.