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Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints

Overview of attention for article published in BMC Bioinformatics, September 2006
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
2 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
107 Dimensions

Readers on

mendeley
78 Mendeley
citeulike
10 CiteULike
connotea
4 Connotea
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Title
Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints
Published in
BMC Bioinformatics, September 2006
DOI 10.1186/1471-2105-7-400
Pubmed ID
Authors

Robin D Dowell, Sean R Eddy

Abstract

We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm.

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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
France 2 3%
Italy 1 1%
Germany 1 1%
Argentina 1 1%
Brazil 1 1%
Greece 1 1%
China 1 1%
Unknown 67 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 28%
Researcher 16 21%
Student > Master 10 13%
Professor > Associate Professor 9 12%
Student > Bachelor 6 8%
Other 10 13%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 46%
Computer Science 12 15%
Biochemistry, Genetics and Molecular Biology 11 14%
Mathematics 2 3%
Immunology and Microbiology 2 3%
Other 8 10%
Unknown 7 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 06 March 2012.
All research outputs
#3,037,101
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,340
of 4,576 outputs
Outputs of similar age
#26,242
of 116,160 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 33 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% 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 116,160 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 77% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.