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ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

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

twitter
2 tweeters
video
1 video uploader

Citations

dimensions_citation
74 Dimensions

Readers on

mendeley
76 Mendeley
citeulike
12 CiteULike
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Title
ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization
Published in
BMC Bioinformatics, January 2009
DOI 10.1186/1471-2105-10-358
Pubmed ID
Authors

Enrico Glaab, Jonathan M Garibaldi, Natalio Krasnogor

Abstract

Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.

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

Geographical breakdown

Country Count As %
Luxembourg 2 3%
Sweden 1 1%
Ireland 1 1%
France 1 1%
United Kingdom 1 1%
Belgium 1 1%
Russia 1 1%
Germany 1 1%
Unknown 67 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 30%
Student > Ph. D. Student 17 22%
Student > Master 8 11%
Student > Bachelor 6 8%
Professor > Associate Professor 5 7%
Other 13 17%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 49%
Medicine and Dentistry 12 16%
Computer Science 11 14%
Biochemistry, Genetics and Molecular Biology 4 5%
Engineering 4 5%
Other 5 7%
Unknown 3 4%

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 18 February 2012.
All research outputs
#7,459,246
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,985
of 4,576 outputs
Outputs of similar age
#61,095
of 116,896 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 33 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% 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 30th percentile – i.e., 30% 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 116,896 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 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.