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Algorithm-driven Artifacts in median polish summarization of Microarray data

Overview of attention for article published in BMC Bioinformatics, November 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
1 tweeter
wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
92 Mendeley
citeulike
4 CiteULike
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Title
Algorithm-driven Artifacts in median polish summarization of Microarray data
Published in
BMC Bioinformatics, November 2010
DOI 10.1186/1471-2105-11-553
Pubmed ID
Authors

Federico M Giorgi, Anthony M Bolger, Marc Lohse, Bjoern Usadel

Abstract

High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification.

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

Geographical breakdown

Country Count As %
Canada 2 2%
United Kingdom 2 2%
Portugal 1 1%
France 1 1%
Switzerland 1 1%
Germany 1 1%
Belgium 1 1%
Denmark 1 1%
Russia 1 1%
Other 1 1%
Unknown 80 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 36%
Researcher 24 26%
Student > Master 9 10%
Other 5 5%
Student > Bachelor 4 4%
Other 8 9%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 42%
Computer Science 14 15%
Biochemistry, Genetics and Molecular Biology 10 11%
Medicine and Dentistry 6 7%
Environmental Science 2 2%
Other 6 7%
Unknown 15 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 January 2021.
All research outputs
#4,019,763
of 19,891,915 outputs
Outputs from BMC Bioinformatics
#1,663
of 6,677 outputs
Outputs of similar age
#25,644
of 129,597 outputs
Outputs of similar age from BMC Bioinformatics
#77
of 314 outputs
Altmetric has tracked 19,891,915 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,677 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 73% 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 129,597 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 314 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.