| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| United States | 1 | 4% |
| India | 1 | 4% |
| France | 1 | 4% |
| Unknown | 21 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 24% |
| Professor | 3 | 12% |
| Student > Postgraduate | 3 | 12% |
| Lecturer | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 6 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 40% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Social Sciences | 2 | 8% |
| Environmental Science | 1 | 4% |
| Linguistics | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 5 | 20% |
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 02 June 2013.
All research outputs
#15,272,611
of 22,711,242 outputs
Outputs from BMC Microbiology
#1,756
of 3,171 outputs
Outputs of similar age
#59,450
of 67,330 outputs
Outputs of similar age from BMC Microbiology
#13
of 15 outputs
Altmetric has tracked 22,711,242 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,171 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.