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Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer

Overview of attention for article published in BMC Bioinformatics, October 2008
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
42 Dimensions

Readers on

mendeley
91 Mendeley
citeulike
12 CiteULike
connotea
1 Connotea
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Title
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer
Published in
BMC Bioinformatics, October 2008
DOI 10.1186/1471-2105-9-462
Pubmed ID
Authors

Raffaele Giancarlo, Davide Scaturro, Filippo Utro

Abstract

Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
France 2 2%
Spain 2 2%
South Africa 1 1%
Sweden 1 1%
United Kingdom 1 1%
China 1 1%
Russia 1 1%
Brazil 1 1%
Other 1 1%
Unknown 77 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 35%
Student > Ph. D. Student 24 26%
Professor > Associate Professor 11 12%
Other 6 7%
Student > Master 5 5%
Other 8 9%
Unknown 5 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 33%
Computer Science 21 23%
Mathematics 10 11%
Medicine and Dentistry 9 10%
Biochemistry, Genetics and Molecular Biology 6 7%
Other 7 8%
Unknown 8 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 02 March 2016.
All research outputs
#6,351,695
of 11,185,066 outputs
Outputs from BMC Bioinformatics
#2,577
of 4,188 outputs
Outputs of similar age
#114,785
of 259,418 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 116 outputs
Altmetric has tracked 11,185,066 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,188 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 34th percentile – i.e., 34% 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 259,418 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.