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Fast-Find: A novel computational approach to analyzing combinatorial motifs

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

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

q&a
1 Q&A thread

Citations

dimensions_citation
425 Dimensions

Readers on

mendeley
22 Mendeley
citeulike
3 CiteULike
connotea
2 Connotea
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Title
Fast-Find: A novel computational approach to analyzing combinatorial motifs
Published in
BMC Bioinformatics, January 2006
DOI 10.1186/1471-2105-7-1
Pubmed ID
Authors

Micah Hamady, Erin Peden, Rob Knight, Ravinder Singh

Abstract

Many vital biological processes, including transcription and splicing, require a combination of short, degenerate sequence patterns, or motifs, adjacent to defined sequence features. Although these motifs occur frequently by chance, they only have biological meaning within a specific context. Identifying transcripts that contain meaningful combinations of patterns is thus an important problem, which existing tools address poorly.

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 9%
United Kingdom 1 5%
Brazil 1 5%
Unknown 18 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 32%
Student > Doctoral Student 2 9%
Lecturer 2 9%
Student > Bachelor 2 9%
Professor > Associate Professor 2 9%
Other 5 23%
Unknown 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 59%
Computer Science 4 18%
Biochemistry, Genetics and Molecular Biology 1 5%
Unknown 4 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 October 2010.
All research outputs
#6,432,477
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,249
of 4,576 outputs
Outputs of similar age
#87,235
of 218,734 outputs
Outputs of similar age from BMC Bioinformatics
#98
of 192 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 218,734 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 59% of its contemporaries.
We're also able to compare this research output to 192 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.