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Reducing the worst case running times of a family of RNA and CFG problems, using Valiant’s approach

Overview of attention for article published in Algorithms for Molecular Biology, August 2011
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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 (#42 of 264)
  • Good Attention Score compared to outputs of the same age (79th percentile)

Mentioned by

blogs
1 blog

Citations

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15 Dimensions

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8 Mendeley
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Title
Reducing the worst case running times of a family of RNA and CFG problems, using Valiant’s approach
Published in
Algorithms for Molecular Biology, August 2011
DOI 10.1186/1748-7188-6-20
Pubmed ID
Authors

Shay Zakov, Dekel Tsur, Michal Ziv-Ukelson

Abstract

RNA secondary structure prediction is a mainstream bioinformatic domain, and is key to computational analysis of functional RNA. In more than 30 years, much research has been devoted to defining different variants of RNA structure prediction problems, and to developing techniques for improving prediction quality. Nevertheless, most of the algorithms in this field follow a similar dynamic programming approach as that presented by Nussinov and Jacobson in the late 70's, which typically yields cubic worst case running time algorithms. Recently, some algorithmic approaches were applied to improve the complexity of these algorithms, motivated by new discoveries in the RNA domain and by the need to efficiently analyze the increasing amount of accumulated genome-wide data.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Student > Master 2 25%
Professor 1 13%
Student > Doctoral Student 1 13%
Researcher 1 13%
Other 0 0%
Readers by discipline Count As %
Computer Science 5 63%
Biochemistry, Genetics and Molecular Biology 1 13%
Agricultural and Biological Sciences 1 13%
Engineering 1 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 15 February 2012.
All research outputs
#4,644,880
of 22,663,150 outputs
Outputs from Algorithms for Molecular Biology
#42
of 264 outputs
Outputs of similar age
#25,419
of 123,308 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 1 outputs
Altmetric has tracked 22,663,150 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 84% 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 123,308 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 79% of its contemporaries.
We're also able to compare this research output to 1 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