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DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment

Overview of attention for article published in BMC Bioinformatics, January 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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
12 X users

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
77 Mendeley
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Title
DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment
Published in
BMC Bioinformatics, January 2020
DOI 10.1186/s12859-019-3190-x
Pubmed ID
Authors

Hiroyuki Fukuda, Kentaro Tomii

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 16 21%
Researcher 14 18%
Student > Master 10 13%
Student > Ph. D. Student 10 13%
Student > Doctoral Student 5 6%
Other 7 9%
Unknown 15 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 23%
Computer Science 16 21%
Agricultural and Biological Sciences 7 9%
Medicine and Dentistry 5 6%
Immunology and Microbiology 2 3%
Other 10 13%
Unknown 19 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 23 December 2020.
All research outputs
#2,331,271
of 23,498,099 outputs
Outputs from BMC Bioinformatics
#651
of 7,400 outputs
Outputs of similar age
#57,502
of 458,827 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 205 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 91% 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 458,827 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 87% of its contemporaries.
We're also able to compare this research output to 205 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.