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Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity

Overview of attention for article published in Algorithms for Molecular Biology, April 2009
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Title
Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity
Published in
Algorithms for Molecular Biology, April 2009
DOI 10.1186/1748-7188-4-7
Pubmed ID
Authors

Koji Kadota, Yuji Nakai, Kentaro Shimizu

Abstract

To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.

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

Geographical breakdown

Country Count As %
Japan 3 3%
United Kingdom 3 3%
Canada 2 2%
United States 2 2%
Germany 2 2%
Russia 1 <1%
Netherlands 1 <1%
Unknown 89 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 38%
Student > Ph. D. Student 23 22%
Professor > Associate Professor 9 9%
Student > Postgraduate 6 6%
Other 6 6%
Other 13 13%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 49%
Computer Science 14 14%
Medicine and Dentistry 11 11%
Engineering 3 3%
Physics and Astronomy 2 2%
Other 14 14%
Unknown 9 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 04 July 2012.
All research outputs
#11,053,202
of 12,434,464 outputs
Outputs from Algorithms for Molecular Biology
#147
of 181 outputs
Outputs of similar age
#102,712
of 119,640 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 4 outputs
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