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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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  • 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 (80th percentile)

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19 X users
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1 Facebook page

Citations

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138 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 138 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 133 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 28%
Student > Ph. D. Student 26 19%
Student > Master 19 14%
Student > Bachelor 10 7%
Student > Doctoral Student 5 4%
Other 19 14%
Unknown 20 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 28%
Biochemistry, Genetics and Molecular Biology 30 22%
Computer Science 17 12%
Medicine and Dentistry 9 7%
Neuroscience 4 3%
Other 16 12%
Unknown 24 17%
Attention Score in Context

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
#3,495,199
of 24,293,076 outputs
Outputs from BMC Bioinformatics
#1,231
of 7,511 outputs
Outputs of similar age
#61,373
of 403,478 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 146 outputs
Altmetric has tracked 24,293,076 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,511 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 83% 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 403,478 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 146 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.