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Partition function and base pairing probabilities of RNA heterodimers

Overview of attention for article published in Algorithms for Molecular Biology, March 2006
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 217)
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

patent
2 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
178 Dimensions

Readers on

mendeley
173 Mendeley
citeulike
8 CiteULike
connotea
2 Connotea
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Title
Partition function and base pairing probabilities of RNA heterodimers
Published in
Algorithms for Molecular Biology, March 2006
DOI 10.1186/1748-7188-1-3
Pubmed ID
Authors

Stephan H Bernhart, Hakim Tafer, Ulrike Mückstein, Christoph Flamm, Peter F Stadler, Ivo L Hofacker

Abstract

RNA has been recognized as a key player in cellular regulation in recent years. In many cases, non-coding RNAs exert their function by binding to other nucleic acids, as in the case of microRNAs and snoRNAs. The specificity of these interactions derives from the stability of inter-molecular base pairing. The accurate computational treatment of RNA-RNA binding therefore lies at the heart of target prediction algorithms.

Mendeley readers

The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
Austria 3 2%
United Kingdom 2 1%
France 1 <1%
New Zealand 1 <1%
Argentina 1 <1%
Denmark 1 <1%
Germany 1 <1%
Unknown 157 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 30%
Researcher 46 27%
Student > Master 17 10%
Student > Bachelor 14 8%
Professor 13 8%
Other 21 12%
Unknown 10 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 80 46%
Biochemistry, Genetics and Molecular Biology 36 21%
Computer Science 23 13%
Chemistry 5 3%
Medicine and Dentistry 5 3%
Other 15 9%
Unknown 9 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 03 April 2019.
All research outputs
#1,951,652
of 14,795,345 outputs
Outputs from Algorithms for Molecular Biology
#17
of 217 outputs
Outputs of similar age
#41,064
of 284,146 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 5 outputs
Altmetric has tracked 14,795,345 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 217 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 92% 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 284,146 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 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them