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Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning With Regulatory Latent Space

Overview of attention for article published in Frontiers in Genetics, September 2020
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
3 X users

Citations

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11 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning With Regulatory Latent Space
Published in
Frontiers in Genetics, September 2020
DOI 10.3389/fgene.2020.00869
Pubmed ID
Authors

Minji Kang, Sangseon Lee, Dohoon Lee, Sun Kim

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Student > Master 4 15%
Student > Bachelor 2 8%
Student > Doctoral Student 1 4%
Professor 1 4%
Other 4 15%
Unknown 8 31%
Readers by discipline Count As %
Computer Science 6 23%
Biochemistry, Genetics and Molecular Biology 3 12%
Medicine and Dentistry 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Agricultural and Biological Sciences 1 4%
Other 3 12%
Unknown 10 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 November 2020.
All research outputs
#15,117,322
of 23,257,423 outputs
Outputs from Frontiers in Genetics
#4,600
of 12,290 outputs
Outputs of similar age
#241,996
of 411,648 outputs
Outputs of similar age from Frontiers in Genetics
#184
of 462 outputs
Altmetric has tracked 23,257,423 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,290 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 55% 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 411,648 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 462 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.