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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 (91st percentile)

Mentioned by

news
1 news outlet
patent
5 patents
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
917 Dimensions

Readers on

mendeley
954 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 954 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 891 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 256 27%
Researcher 224 23%
Student > Master 111 12%
Student > Doctoral Student 60 6%
Student > Bachelor 50 5%
Other 132 14%
Unknown 121 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 324 34%
Biochemistry, Genetics and Molecular Biology 203 21%
Medicine and Dentistry 75 8%
Computer Science 43 5%
Immunology and Microbiology 42 4%
Other 114 12%
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
#1,628,918
of 20,192,041 outputs
Outputs from BMC Bioinformatics
#433
of 6,740 outputs
Outputs of similar age
#26,353
of 314,199 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 20,192,041 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 6,740 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. 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 314,199 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 91% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them