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Mendeley readers
Attention Score in Context
Title |
Computing minimal nutrient sets from metabolic networks via linear constraint solving
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Published in |
BMC Bioinformatics, March 2013
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DOI | 10.1186/1471-2105-14-114 |
Pubmed ID | |
Authors |
Steven Eker, Markus Krummenacker, Alexander G Shearer, Ashish Tiwari, Ingrid M Keseler, Carolyn Talcott, Peter D Karp |
Abstract |
As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Thailand | 1 | 3% |
Belgium | 1 | 3% |
Unknown | 36 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 23% |
Student > Ph. D. Student | 9 | 23% |
Student > Master | 6 | 15% |
Professor > Associate Professor | 5 | 13% |
Student > Doctoral Student | 3 | 8% |
Other | 4 | 10% |
Unknown | 3 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 18 | 46% |
Computer Science | 9 | 23% |
Engineering | 3 | 8% |
Biochemistry, Genetics and Molecular Biology | 1 | 3% |
Unspecified | 1 | 3% |
Other | 2 | 5% |
Unknown | 5 | 13% |
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 10 December 2013.
All research outputs
#14,748,737
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#5,033
of 7,254 outputs
Outputs of similar age
#118,996
of 197,839 outputs
Outputs of similar age from BMC Bioinformatics
#104
of 145 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 197,839 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.