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NEAT: an efficient network enrichment analysis test

Overview of attention for article published in BMC Bioinformatics, September 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

twitter
12 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
76 Mendeley
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Title
NEAT: an efficient network enrichment analysis test
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1203-6
Pubmed ID
Authors

Mirko Signorelli, Veronica Vinciotti, Ernst C. Wit

Abstract

Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

Twitter Demographics

The data shown below were collected from the profiles of 12 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 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 1%
Norway 1 1%
Brazil 1 1%
Unknown 73 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 26%
Student > Ph. D. Student 17 22%
Student > Master 14 18%
Student > Bachelor 10 13%
Professor 5 7%
Other 6 8%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 32%
Biochemistry, Genetics and Molecular Biology 23 30%
Computer Science 9 12%
Medicine and Dentistry 4 5%
Engineering 3 4%
Other 5 7%
Unknown 8 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 17 October 2020.
All research outputs
#3,816,587
of 22,498,148 outputs
Outputs from BMC Bioinformatics
#1,454
of 7,197 outputs
Outputs of similar age
#58,042
of 289,053 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 29 outputs
Altmetric has tracked 22,498,148 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,197 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 well, scoring higher than 79% 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 289,053 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.