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MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins

Overview of attention for article published in BMC Bioinformatics, May 2012
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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 (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
3 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
202 Dimensions

Readers on

mendeley
143 Mendeley
citeulike
4 CiteULike
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Title
MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins
Published in
BMC Bioinformatics, May 2012
DOI 10.1186/1471-2105-13-111
Pubmed ID
Authors

Lukasz P Kozlowski, Janusz M Bujnicki

Abstract

Intrinsically unstructured proteins (IUPs) lack a well-defined three-dimensional structure. Some of them may assume a locally stable structure under specific conditions, e.g. upon interaction with another molecule, while others function in a permanently unstructured state. The discovery of IUPs challenged the traditional protein structure paradigm, which stated that a specific well-defined structure defines the function of the protein. As of December 2011, approximately 60 methods for computational prediction of protein disorder from sequence have been made publicly available. They are based on different approaches, such as utilizing evolutionary information, energy functions, and various statistical and machine learning methods.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Poland 4 3%
United Kingdom 2 1%
United States 2 1%
Japan 1 <1%
Chile 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 131 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 31%
Student > Master 25 17%
Researcher 21 15%
Student > Bachelor 14 10%
Student > Doctoral Student 5 3%
Other 19 13%
Unknown 15 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 44%
Biochemistry, Genetics and Molecular Biology 42 29%
Chemistry 10 7%
Computer Science 3 2%
Chemical Engineering 2 1%
Other 6 4%
Unknown 17 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 December 2014.
All research outputs
#674,526
of 4,586,246 outputs
Outputs from BMC Bioinformatics
#560
of 2,673 outputs
Outputs of similar age
#10,768
of 75,880 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 97 outputs
Altmetric has tracked 4,586,246 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,673 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 78% 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 75,880 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 85% of its contemporaries.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.