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Identification of Enterobacter sakazakii from closely related species: The use of Artificial Neural Networks in the analysis of biochemical and 16S rDNA data

Overview of attention for article published in BMC Microbiology, March 2006
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

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24 Dimensions

Readers on

mendeley
21 Mendeley
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Title
Identification of Enterobacter sakazakii from closely related species: The use of Artificial Neural Networks in the analysis of biochemical and 16S rDNA data
Published in
BMC Microbiology, March 2006
DOI 10.1186/1471-2180-6-28
Pubmed ID
Authors

Carol Iversen, Lee Lancashire, Michael Waddington, Stephen Forsythe, Graham Ball

Abstract

Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
United States 1 5%
India 1 5%
France 1 5%
Unknown 17 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 29%
Professor > Associate Professor 2 10%
Professor 2 10%
Lecturer 2 10%
Student > Postgraduate 2 10%
Other 3 14%
Unknown 4 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 48%
Biochemistry, Genetics and Molecular Biology 2 10%
Environmental Science 1 5%
Linguistics 1 5%
Computer Science 1 5%
Other 2 10%
Unknown 4 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 03 June 2013.
All research outputs
#10,009,219
of 17,388,379 outputs
Outputs from BMC Microbiology
#1,078
of 2,642 outputs
Outputs of similar age
#80,392
of 163,844 outputs
Outputs of similar age from BMC Microbiology
#1
of 1 outputs
Altmetric has tracked 17,388,379 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,642 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 58% 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 163,844 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
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