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Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set-

Overview of attention for article published in BMC Bioinformatics, January 2016
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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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
20 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
49 Dimensions

Readers on

mendeley
128 Mendeley
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Title
Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0884-1
Pubmed ID
Authors

Ludwig Geistlinger, Gergely Csaba, Ralf Zimmer

Abstract

Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation. We present the EnrichmentBrowser package as an easily applicable software that enables (1) the application of the most frequently used set-based and network-based enrichment methods, (2) their straightforward combination, and (3) a detailed and interactive visualization and exploration of the results. The package is available from the Bioconductor repository and implements additional support for standardized expression data preprocessing, differential expression analysis, and definition of suitable input gene sets and networks. The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. It combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

Twitter Demographics

The data shown below were collected from the profiles of 20 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 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 123 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 29%
Student > Ph. D. Student 25 20%
Student > Master 17 13%
Student > Bachelor 10 8%
Student > Doctoral Student 5 4%
Other 17 13%
Unknown 17 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 29%
Biochemistry, Genetics and Molecular Biology 28 22%
Computer Science 17 13%
Medicine and Dentistry 8 6%
Engineering 4 3%
Other 12 9%
Unknown 22 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 28 July 2020.
All research outputs
#2,462,717
of 18,439,562 outputs
Outputs from BMC Bioinformatics
#961
of 6,391 outputs
Outputs of similar age
#52,337
of 353,732 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 7 outputs
Altmetric has tracked 18,439,562 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,391 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 84% 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 353,732 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 84% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.