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GAGE: generally applicable gene set enrichment for pathway analysis

Overview of attention for article published in BMC Bioinformatics, May 2009
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
1 news outlet
patent
5 patents
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
941 Dimensions

Readers on

mendeley
971 Mendeley
citeulike
25 CiteULike
connotea
1 Connotea
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Title
GAGE: generally applicable gene set enrichment for pathway analysis
Published in
BMC Bioinformatics, May 2009
DOI 10.1186/1471-2105-10-161
Pubmed ID
Authors

Weijun Luo, Michael S Friedman, Kerby Shedden, Kurt D Hankenson, Peter J Woolf

Abstract

Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.

Mendeley readers

The data shown below were compiled from readership statistics for 971 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 2%
United Kingdom 14 1%
Germany 6 <1%
Sweden 3 <1%
Netherlands 2 <1%
Portugal 2 <1%
Korea, Republic of 2 <1%
Italy 2 <1%
Russia 2 <1%
Other 13 1%
Unknown 908 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 256 26%
Researcher 227 23%
Student > Master 112 12%
Student > Doctoral Student 62 6%
Student > Bachelor 50 5%
Other 143 15%
Unknown 121 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 327 34%
Biochemistry, Genetics and Molecular Biology 207 21%
Medicine and Dentistry 75 8%
Computer Science 44 5%
Immunology and Microbiology 42 4%
Other 123 13%
Unknown 153 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 04 June 2020.
All research outputs
#1,886,937
of 22,787,797 outputs
Outputs from BMC Bioinformatics
#467
of 7,279 outputs
Outputs of similar age
#6,479
of 111,865 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 36 outputs
Altmetric has tracked 22,787,797 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
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 done particularly well, scoring higher than 93% 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 111,865 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 94% of its contemporaries.
We're also able to compare this research output to 36 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.