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DNdisorder: predicting protein disorder using boosting and deep networks

Overview of attention for article published in BMC Bioinformatics, March 2013
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
13 X users

Citations

dimensions_citation
75 Dimensions

Readers on

mendeley
108 Mendeley
citeulike
2 CiteULike
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Title
DNdisorder: predicting protein disorder using boosting and deep networks
Published in
BMC Bioinformatics, March 2013
DOI 10.1186/1471-2105-14-88
Pubmed ID
Authors

Jesse Eickholt, Jianlin Cheng

Abstract

A number of proteins contain regions which do not adopt a stable tertiary structure in their native state. Such regions known as disordered regions have been shown to participate in many vital cell functions and are increasingly being examined as drug targets.

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 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 <1%
Italy 1 <1%
Israel 1 <1%
Canada 1 <1%
United States 1 <1%
Unknown 103 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 28%
Student > Master 19 18%
Researcher 16 15%
Student > Bachelor 8 7%
Student > Doctoral Student 6 6%
Other 15 14%
Unknown 14 13%
Readers by discipline Count As %
Computer Science 28 26%
Agricultural and Biological Sciences 28 26%
Biochemistry, Genetics and Molecular Biology 20 19%
Engineering 4 4%
Chemistry 4 4%
Other 6 6%
Unknown 18 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 30 September 2021.
All research outputs
#2,126,846
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#533
of 7,454 outputs
Outputs of similar age
#17,459
of 197,273 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 141 outputs
Altmetric has tracked 23,881,329 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 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 92% 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 197,273 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 141 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 92% of its contemporaries.