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The scoring of poses in protein-protein docking: current capabilities and future directions

Overview of attention for article published in BMC Bioinformatics, October 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
86 Dimensions

Readers on

mendeley
111 Mendeley
citeulike
3 CiteULike
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Title
The scoring of poses in protein-protein docking: current capabilities and future directions
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-286
Pubmed ID
Authors

Iain H Moal, Mieczyslaw Torchala, Paul A Bates, Juan Fernández-Recio

Abstract

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
France 1 <1%
Korea, Republic of 1 <1%
Brazil 1 <1%
Finland 1 <1%
United Kingdom 1 <1%
Iran, Islamic Republic of 1 <1%
Romania 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 101 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 30%
Researcher 26 23%
Student > Master 13 12%
Student > Bachelor 7 6%
Professor > Associate Professor 5 5%
Other 16 14%
Unknown 11 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 32%
Biochemistry, Genetics and Molecular Biology 23 21%
Computer Science 13 12%
Chemistry 9 8%
Engineering 5 5%
Other 12 11%
Unknown 14 13%

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 01 October 2013.
All research outputs
#11,957,006
of 21,347,834 outputs
Outputs from BMC Bioinformatics
#3,473
of 6,923 outputs
Outputs of similar age
#90,436
of 187,778 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 37 outputs
Altmetric has tracked 21,347,834 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,923 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 48th percentile – i.e., 48% 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 187,778 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 51% of its contemporaries.
We're also able to compare this research output to 37 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 64% of its contemporaries.