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Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data

Overview of attention for article published in BMC Bioinformatics, August 2004
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Mentioned by

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1 patent

Citations

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231 Dimensions

Readers on

mendeley
203 Mendeley
citeulike
8 CiteULike
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2 Connotea
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Title
Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data
Published in
BMC Bioinformatics, August 2004
DOI 10.1186/1471-2105-5-118
Pubmed ID
Authors

Carsten O Daub, Ralf Steuer, Joachim Selbig, Sebastian Kloska

Abstract

The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. In the context of the clustering of genes with similar patterns of expression it has been suggested as a general quantity of similarity to extend commonly used linear measures. Since mutual information is defined in terms of discrete variables, its application to continuous data requires the use of binning procedures, which can lead to significant numerical errors for datasets of small or moderate size.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 203 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Germany 2 <1%
Belgium 2 <1%
Chile 1 <1%
France 1 <1%
Italy 1 <1%
Hong Kong 1 <1%
Brazil 1 <1%
Israel 1 <1%
Other 10 5%
Unknown 178 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 63 31%
Student > Ph. D. Student 51 25%
Student > Master 26 13%
Professor > Associate Professor 13 6%
Professor 10 5%
Other 25 12%
Unknown 15 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 33%
Computer Science 37 18%
Engineering 23 11%
Biochemistry, Genetics and Molecular Biology 17 8%
Mathematics 12 6%
Other 24 12%
Unknown 23 11%
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 10 December 2015.
All research outputs
#8,475,076
of 25,287,709 outputs
Outputs from BMC Bioinformatics
#3,213
of 7,672 outputs
Outputs of similar age
#22,655
of 69,649 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 25,287,709 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,672 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 gotten more attention than average, scoring higher than 50% 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 69,649 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them