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MarVis: a tool for clustering and visualization of metabolic biomarkers

Overview of attention for article published in BMC Bioinformatics, March 2009
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Mentioned by

twitter
1 tweeter

Citations

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33 Dimensions

Readers on

mendeley
60 Mendeley
citeulike
4 CiteULike
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Title
MarVis: a tool for clustering and visualization of metabolic biomarkers
Published in
BMC Bioinformatics, March 2009
DOI 10.1186/1471-2105-10-92
Pubmed ID
Authors

Alexander Kaever, Thomas Lingner, Kirstin Feussner, Cornelia Göbel, Ivo Feussner, Peter Meinicke

Abstract

A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.

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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 7%
Colombia 1 2%
Unknown 55 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 25%
Student > Ph. D. Student 15 25%
Student > Bachelor 8 13%
Professor > Associate Professor 4 7%
Student > Doctoral Student 3 5%
Other 9 15%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 43%
Biochemistry, Genetics and Molecular Biology 10 17%
Computer Science 5 8%
Engineering 3 5%
Immunology and Microbiology 2 3%
Other 6 10%
Unknown 8 13%

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 10 July 2012.
All research outputs
#7,762,551
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,576 outputs
Outputs of similar age
#68,086
of 120,864 outputs
Outputs of similar age from BMC Bioinformatics
#31
of 46 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 120,864 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.