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Comparison of co-expression measures: mutual information, correlation, and model based indices

Overview of attention for article published in BMC Bioinformatics, December 2012
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
9 X users
patent
1 patent
wikipedia
3 Wikipedia pages
f1000
1 research highlight platform

Citations

dimensions_citation
363 Dimensions

Readers on

mendeley
565 Mendeley
citeulike
13 CiteULike
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Title
Comparison of co-expression measures: mutual information, correlation, and model based indices
Published in
BMC Bioinformatics, December 2012
DOI 10.1186/1471-2105-13-328
Pubmed ID
Authors

Lin Song, Peter Langfelder, Steve Horvath

Abstract

Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes).

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 565 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 16 3%
United Kingdom 3 <1%
France 3 <1%
Netherlands 2 <1%
Brazil 2 <1%
Sweden 2 <1%
Italy 1 <1%
Australia 1 <1%
Norway 1 <1%
Other 7 1%
Unknown 527 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 165 29%
Researcher 106 19%
Student > Master 70 12%
Student > Bachelor 34 6%
Student > Doctoral Student 29 5%
Other 89 16%
Unknown 72 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 187 33%
Biochemistry, Genetics and Molecular Biology 104 18%
Computer Science 61 11%
Mathematics 25 4%
Engineering 21 4%
Other 73 13%
Unknown 94 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 08 December 2022.
All research outputs
#2,715,226
of 23,636,051 outputs
Outputs from BMC Bioinformatics
#834
of 7,411 outputs
Outputs of similar age
#26,982
of 282,719 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 139 outputs
Altmetric has tracked 23,636,051 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,411 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 88% 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 282,719 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.