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Mining SOM expression portraits: feature selection and integrating concepts of molecular function

Overview of attention for article published in BioData Mining, October 2012
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Mentioned by

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1 tweeter

Citations

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49 Dimensions

Readers on

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58 Mendeley
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Title
Mining SOM expression portraits: feature selection and integrating concepts of molecular function
Published in
BioData Mining, October 2012
DOI 10.1186/1756-0381-5-18
Pubmed ID
Authors

Henry Wirth, Martin von Bergen, Hans Binder

Abstract

Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Germany 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 24%
Student > Ph. D. Student 8 14%
Student > Bachelor 8 14%
Student > Master 8 14%
Student > Postgraduate 4 7%
Other 10 17%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 34%
Biochemistry, Genetics and Molecular Biology 13 22%
Computer Science 11 19%
Mathematics 2 3%
Immunology and Microbiology 2 3%
Other 5 9%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 11 November 2012.
All research outputs
#9,954,327
of 12,434,464 outputs
Outputs from BioData Mining
#194
of 224 outputs
Outputs of similar age
#98,267
of 139,598 outputs
Outputs of similar age from BioData Mining
#8
of 8 outputs
Altmetric has tracked 12,434,464 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 224 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one is in the 5th percentile – i.e., 5% 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 139,598 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.