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Stratification bias in low signal microarray studies

Overview of attention for article published in BMC Bioinformatics, September 2007
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1 tweeter

Citations

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43 Dimensions

Readers on

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77 Mendeley
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6 CiteULike
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Title
Stratification bias in low signal microarray studies
Published in
BMC Bioinformatics, September 2007
DOI 10.1186/1471-2105-8-326
Pubmed ID
Authors

Brian J Parker, Simon Günter, Justin Bedo

Abstract

When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated.

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

Geographical breakdown

Country Count As %
United States 4 5%
Australia 2 3%
France 1 1%
Switzerland 1 1%
United Kingdom 1 1%
Ukraine 1 1%
China 1 1%
Germany 1 1%
Unknown 65 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 31%
Researcher 18 23%
Student > Master 10 13%
Student > Bachelor 6 8%
Professor > Associate Professor 4 5%
Other 9 12%
Unknown 6 8%
Readers by discipline Count As %
Computer Science 16 21%
Agricultural and Biological Sciences 14 18%
Engineering 10 13%
Medicine and Dentistry 8 10%
Biochemistry, Genetics and Molecular Biology 6 8%
Other 12 16%
Unknown 11 14%

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 30 October 2013.
All research outputs
#8,355,214
of 10,604,514 outputs
Outputs from BMC Bioinformatics
#3,494
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Outputs of similar age
#101,668
of 153,313 outputs
Outputs of similar age from BMC Bioinformatics
#87
of 108 outputs
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