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Statistical methods for analyzing immunosignatures

Overview of attention for article published in BMC Bioinformatics, August 2011
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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 (90th percentile)

Mentioned by

twitter
1 tweeter
patent
4 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
38 Mendeley
citeulike
1 CiteULike
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Title
Statistical methods for analyzing immunosignatures
Published in
BMC Bioinformatics, August 2011
DOI 10.1186/1471-2105-12-349
Pubmed ID
Authors

Justin R Brown, Phillip Stafford, Stephen A Johnston, Valentin Dinu

Abstract

Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.

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

Geographical breakdown

Country Count As %
United States 2 5%
United Kingdom 1 3%
Unknown 35 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 34%
Student > Ph. D. Student 11 29%
Student > Master 3 8%
Professor 2 5%
Other 2 5%
Other 3 8%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 29%
Biochemistry, Genetics and Molecular Biology 7 18%
Engineering 4 11%
Immunology and Microbiology 4 11%
Medicine and Dentistry 4 11%
Other 4 11%
Unknown 4 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 01 September 2020.
All research outputs
#1,899,064
of 18,783,186 outputs
Outputs from BMC Bioinformatics
#629
of 6,429 outputs
Outputs of similar age
#10,206
of 105,788 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 18,783,186 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 6,429 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 90% 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 105,788 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 90% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them