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

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6 X users

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116 Mendeley
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 116 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 106 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 29%
Researcher 27 23%
Student > Master 12 10%
Student > Bachelor 7 6%
Student > Doctoral Student 5 4%
Other 16 14%
Unknown 15 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 29%
Biochemistry, Genetics and Molecular Biology 24 21%
Computer Science 13 11%
Chemistry 10 9%
Engineering 5 4%
Other 13 11%
Unknown 17 15%
Attention Score in Context

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
#13,363,602
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#3,690
of 7,454 outputs
Outputs of similar age
#105,654
of 209,974 outputs
Outputs of similar age from BMC Bioinformatics
#43
of 99 outputs
Altmetric has tracked 23,881,329 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 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 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 209,974 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 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 57% of its contemporaries.