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Computing expectation values for RNA motifs using discrete convolutions

Overview of attention for article published in BMC Bioinformatics, May 2005
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 X user
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1 Wikipedia page

Citations

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7 Dimensions

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9 Mendeley
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Title
Computing expectation values for RNA motifs using discrete convolutions
Published in
BMC Bioinformatics, May 2005
DOI 10.1186/1471-2105-6-118
Pubmed ID
Authors

André Lambert, Matthieu Legendre, Jean-Fred Fontaine, Daniel Gautheret

Abstract

Computational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motifs defined by single-strand and helix lod-score profiles with variable helix spans. Such E-values cannot be computed assuming a normal score distribution and their estimation previously required lengthy simulations.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 33%
Professor 2 22%
Student > Ph. D. Student 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Other 1 11%
Readers by discipline Count As %
Computer Science 3 33%
Agricultural and Biological Sciences 3 33%
Engineering 2 22%
Biochemistry, Genetics and Molecular Biology 1 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 June 2020.
All research outputs
#7,209,728
of 22,788,370 outputs
Outputs from BMC Bioinformatics
#2,862
of 7,279 outputs
Outputs of similar age
#19,962
of 58,223 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 23 outputs
Altmetric has tracked 22,788,370 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 59% 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 58,223 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.