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DARS-RNP and QUASI-RNP: New statistical potentials for protein-RNA docking

Overview of attention for article published in BMC Bioinformatics, August 2011
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2 tweeters

Citations

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Title
DARS-RNP and QUASI-RNP: New statistical potentials for protein-RNA docking
Published in
BMC Bioinformatics, August 2011
DOI 10.1186/1471-2105-12-348
Pubmed ID
Authors

Irina Tuszynska, Janusz M Bujnicki

Abstract

Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 2%
Germany 1 1%
Australia 1 1%
United Kingdom 1 1%
United States 1 1%
Poland 1 1%
Unknown 74 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 33%
Researcher 16 20%
Professor > Associate Professor 6 7%
Student > Master 6 7%
Student > Postgraduate 4 5%
Other 11 14%
Unknown 11 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 38%
Biochemistry, Genetics and Molecular Biology 14 17%
Computer Science 9 11%
Chemistry 9 11%
Engineering 2 2%
Other 6 7%
Unknown 10 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 21 November 2012.
All research outputs
#9,508,785
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,587
of 4,576 outputs
Outputs of similar age
#68,226
of 89,677 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 19 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% 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 89,677 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.