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Evolving stochastic context--free grammars for RNA secondary structure prediction

Overview of attention for article published in BMC Bioinformatics, January 2012
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Title
Evolving stochastic context--free grammars for RNA secondary structure prediction
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-78
Pubmed ID
Authors

James WJ Anderson, Paula Tataru, Joe Staines, Jotun Hein, Rune Lyngsø

Abstract

ABSTRACT: BACKGROUND: Stochastic Context-Free Grammars (SCFGs) were applied successfully to RNA secondary structure prediction in the early 90s, and used in combination with comparative methods in the late 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is very large, but a few intuitively designed grammars have remained dominant. In this paper we investigate two automatic search techniques for effective grammars - exhaustive search for very compact grammars and an evolutionary algorithm to find larger grammars. We also examine whether grammar ambiguity is as problematic to structure prediction as has been previously suggested. RESULTS: These search techniques were applied to predict RNA secondary structure on a maximal data set and revealed new and interesting grammars, though none are dramatically better than classic grammars. In general, results showed that many grammars with quite different structure could have very similar predictive ability. Many ambiguous grammars were found which were at least as effective as the best current unambiguous grammars. CONCLUSIONS: Overall the method of evolving SCFGs for RNA secondary structure prediction proved effective in finding many grammars that had strong predictive accuracy, as good or slightly better than those designed manually. Furthermore, several of the best grammars found were ambiguous, demonstrating that such grammars should not be disregarded.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 5%
Netherlands 1 3%
United States 1 3%
Sweden 1 3%
New Zealand 1 3%
Unknown 31 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 30%
Student > Ph. D. Student 8 22%
Student > Master 7 19%
Professor > Associate Professor 3 8%
Unspecified 2 5%
Other 6 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 35%
Computer Science 9 24%
Biochemistry, Genetics and Molecular Biology 4 11%
Engineering 4 11%
Unspecified 2 5%
Other 5 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 May 2012.
All research outputs
#7,459,259
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,989
of 4,588 outputs
Outputs of similar age
#61,991
of 118,112 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 7 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.