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gsGator: an integrated web platform for cross-species gene set analysis

Overview of attention for article published in BMC Bioinformatics, January 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
26 Mendeley
citeulike
3 CiteULike
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Title
gsGator: an integrated web platform for cross-species gene set analysis
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-13
Pubmed ID
Authors

Hyunjung Kang, Ikjung Choi, Sooyoung Cho, Daeun Ryu, Sanghyuk Lee, Wankyu Kim

Abstract

Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Russia 1 4%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 35%
Student > Ph. D. Student 5 19%
Professor > Associate Professor 3 12%
Student > Postgraduate 2 8%
Student > Bachelor 2 8%
Other 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 58%
Biochemistry, Genetics and Molecular Biology 5 19%
Computer Science 2 8%
Medicine and Dentistry 1 4%
Chemistry 1 4%
Other 1 4%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 January 2014.
All research outputs
#8,207,194
of 14,571,674 outputs
Outputs from BMC Bioinformatics
#3,100
of 5,418 outputs
Outputs of similar age
#114,202
of 260,555 outputs
Outputs of similar age from BMC Bioinformatics
#224
of 410 outputs
Altmetric has tracked 14,571,674 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,418 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 40th percentile – i.e., 40% 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 260,555 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 54% of its contemporaries.
We're also able to compare this research output to 410 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.