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A reexamination of information theory-based methods for DNA-binding site identification

Overview of attention for article published in BMC Bioinformatics, February 2009
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
1 X user
wikipedia
6 Wikipedia pages

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
78 Mendeley
citeulike
4 CiteULike
connotea
3 Connotea
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Title
A reexamination of information theory-based methods for DNA-binding site identification
Published in
BMC Bioinformatics, February 2009
DOI 10.1186/1471-2105-10-57
Pubmed ID
Authors

Ivan Erill, Michael C O'Neill

Abstract

Searching for transcription factor binding sites in genome sequences is still an open problem in bioinformatics. Despite substantial progress, search methods based on information theory remain a standard in the field, even though the full validity of their underlying assumptions has only been tested in artificial settings. Here we use newly available data on transcription factors from different bacterial genomes to make a more thorough assessment of information theory-based search methods.

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

Geographical breakdown

Country Count As %
Germany 2 3%
Malaysia 2 3%
United Kingdom 2 3%
Korea, Republic of 1 1%
Turkey 1 1%
Canada 1 1%
Belgium 1 1%
Spain 1 1%
Poland 1 1%
Other 1 1%
Unknown 65 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 31%
Researcher 19 24%
Student > Bachelor 11 14%
Professor > Associate Professor 5 6%
Student > Master 5 6%
Other 11 14%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 45%
Computer Science 15 19%
Biochemistry, Genetics and Molecular Biology 11 14%
Engineering 3 4%
Physics and Astronomy 3 4%
Other 7 9%
Unknown 4 5%
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 09 January 2023.
All research outputs
#7,590,771
of 24,410,160 outputs
Outputs from BMC Bioinformatics
#2,811
of 7,530 outputs
Outputs of similar age
#49,755
of 180,611 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 56 outputs
Altmetric has tracked 24,410,160 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,530 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 60% 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 180,611 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 71% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.