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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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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 89 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 82 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 30%
Researcher 18 20%
Student > Master 8 9%
Professor > Associate Professor 7 8%
Student > Postgraduate 4 4%
Other 11 12%
Unknown 14 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 34%
Biochemistry, Genetics and Molecular Biology 20 22%
Chemistry 10 11%
Computer Science 9 10%
Engineering 2 2%
Other 6 7%
Unknown 12 13%
Attention Score in Context

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
#17,645,074
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#5,909
of 7,234 outputs
Outputs of similar age
#99,851
of 123,300 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 71 outputs
Altmetric has tracked 22,649,029 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 7,234 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 13th percentile – i.e., 13% 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 123,300 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.