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Network-enabled gene expression analysis

Overview of attention for article published in BMC Bioinformatics, July 2012
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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3 X users

Citations

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16 Dimensions

Readers on

mendeley
63 Mendeley
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6 CiteULike
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Title
Network-enabled gene expression analysis
Published in
BMC Bioinformatics, July 2012
DOI 10.1186/1471-2105-13-167
Pubmed ID
Authors

David Edwards, Lei Wang, Peter Sørensen

Abstract

Although genome-scale expression experiments are performed routinely in biomedical research, methods of analysis remain simplistic and their interpretation challenging. The conventional approach is to compare the expression of each gene, one at a time, between treatment groups. This implicitly treats the gene expression levels as independent, but they are in fact highly interdependent, and exploiting this enables substantial power gains to be realized.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
United Kingdom 2 3%
India 1 2%
Japan 1 2%
Netherlands 1 2%
Unknown 55 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 40%
Student > Ph. D. Student 13 21%
Professor 5 8%
Professor > Associate Professor 5 8%
Student > Master 5 8%
Other 6 10%
Unknown 4 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 43%
Computer Science 10 16%
Biochemistry, Genetics and Molecular Biology 9 14%
Engineering 3 5%
Mathematics 2 3%
Other 3 5%
Unknown 9 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 October 2012.
All research outputs
#12,857,407
of 22,671,366 outputs
Outputs from BMC Bioinformatics
#3,778
of 7,247 outputs
Outputs of similar age
#87,262
of 163,495 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 93 outputs
Altmetric has tracked 22,671,366 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 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% 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 163,495 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.