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GONOME: measuring correlations between GO terms and genomic positions

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
9 X users
q&a
2 Q&A threads

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
49 Mendeley
citeulike
6 CiteULike
connotea
3 Connotea
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Title
GONOME: measuring correlations between GO terms and genomic positions
Published in
BMC Bioinformatics, February 2006
DOI 10.1186/1471-2105-7-94
Pubmed ID
Authors

Stefan M Stanley, Timothy L Bailey, John S Mattick

Abstract

Current methods to find significantly under- and over-represented gene ontology (GO) terms in a set of genes consider the genes as equally probable "balls in a bag", as may be appropriate for transcripts in micro-array data. However, due to the varying length of genes and intergenic regions, that approach is inappropriate for deciding if any GO terms are correlated with a set of genomic positions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 6%
Australia 2 4%
Italy 1 2%
Canada 1 2%
Greece 1 2%
United States 1 2%
Unknown 40 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 33%
Student > Ph. D. Student 11 22%
Professor > Associate Professor 6 12%
Professor 3 6%
Other 3 6%
Other 7 14%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 61%
Biochemistry, Genetics and Molecular Biology 5 10%
Medicine and Dentistry 3 6%
Computer Science 2 4%
Engineering 2 4%
Other 2 4%
Unknown 5 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 14 August 2014.
All research outputs
#3,627,321
of 25,401,784 outputs
Outputs from BMC Bioinformatics
#1,213
of 7,701 outputs
Outputs of similar age
#8,958
of 92,655 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 61 outputs
Altmetric has tracked 25,401,784 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,701 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 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 92,655 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.