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GSVA: gene set variation analysis for microarray and RNA-Seq data

Overview of attention for article published in BMC Bioinformatics, January 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
2 blogs
twitter
16 tweeters
patent
9 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
875 Dimensions

Readers on

mendeley
953 Mendeley
citeulike
9 CiteULike
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Title
GSVA: gene set variation analysis for microarray and RNA-Seq data
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-7
Pubmed ID
Authors

Sonja Hänzelmann, Robert Castelo, Justin Guinney

Abstract

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 953 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 1%
Spain 6 <1%
United Kingdom 6 <1%
Germany 3 <1%
Australia 3 <1%
Brazil 2 <1%
Sweden 2 <1%
Netherlands 1 <1%
India 1 <1%
Other 6 <1%
Unknown 910 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 288 30%
Student > Ph. D. Student 221 23%
Student > Master 102 11%
Student > Bachelor 62 7%
Other 41 4%
Other 135 14%
Unknown 104 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 281 29%
Biochemistry, Genetics and Molecular Biology 227 24%
Medicine and Dentistry 100 10%
Computer Science 77 8%
Engineering 28 3%
Other 103 11%
Unknown 137 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 10 December 2019.
All research outputs
#690,130
of 15,184,505 outputs
Outputs from BMC Bioinformatics
#95
of 5,572 outputs
Outputs of similar age
#10,017
of 251,488 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 194 outputs
Altmetric has tracked 15,184,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,572 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 98% 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 251,488 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 96% of its contemporaries.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.