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Application of an interpretable classification model on Early Folding Residues during protein folding

Overview of attention for article published in BioData Mining, January 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
17 Mendeley
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Title
Application of an interpretable classification model on Early Folding Residues during protein folding
Published in
BioData Mining, January 2019
DOI 10.1186/s13040-018-0188-2
Authors

Sebastian Bittrich, Marika Kaden, Christoph Leberecht, Florian Kaiser, Thomas Villmann, Dirk Labudde

Twitter Demographics

The data shown below were collected from the profiles of 4 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 41%
Student > Ph. D. Student 3 18%
Researcher 1 6%
Student > Bachelor 1 6%
Unknown 5 29%
Readers by discipline Count As %
Computer Science 7 41%
Engineering 4 24%
Biochemistry, Genetics and Molecular Biology 1 6%
Unknown 5 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 January 2019.
All research outputs
#7,235,494
of 14,122,258 outputs
Outputs from BioData Mining
#143
of 241 outputs
Outputs of similar age
#154,441
of 368,480 outputs
Outputs of similar age from BioData Mining
#12
of 21 outputs
Altmetric has tracked 14,122,258 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 241 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. 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 368,480 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 57% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.