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DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data

Overview of attention for article published in Genome Biology, September 2007
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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26 X users
facebook
1 Facebook page

Citations

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25 Dimensions

Readers on

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27 Mendeley
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1 CiteULike
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Title
DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data
Published in
Genome Biology, September 2007
DOI 10.1186/gb-2007-8-9-r190
Pubmed ID
Authors

Michael Watson, Juliet Dukes, Abu-Bakr Abu-Median, Donald P King, Paul Britton

Abstract

DNA microarrays offer the possibility of testing for the presence of thousands of micro-organisms in a single experiment. However, there is a lack of reliable bioinformatics tools for the analysis of such data. We have developed DetectiV, a package for the statistical software R. DetectiV offers powerful yet simple visualization, normalization and significance testing tools. We show that DetectiV performs better than previously published software on a large, publicly available dataset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 7%
United States 1 4%
Australia 1 4%
Unknown 23 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 33%
Student > Ph. D. Student 6 22%
Professor 4 15%
Professor > Associate Professor 2 7%
Librarian 1 4%
Other 2 7%
Unknown 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 48%
Veterinary Science and Veterinary Medicine 2 7%
Computer Science 2 7%
Engineering 2 7%
Chemical Engineering 1 4%
Other 4 15%
Unknown 3 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 June 2021.
All research outputs
#2,590,266
of 25,374,917 outputs
Outputs from Genome Biology
#2,071
of 4,467 outputs
Outputs of similar age
#5,909
of 82,572 outputs
Outputs of similar age from Genome Biology
#5
of 38 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 53% 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 82,572 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.