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Article details
Title
Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface
Published in
BMC Bioinformatics, May 2011
DOI 10.1186/1471-2105-12-196
Pubmed ID
Authors
Abstract

Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment.

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X Demographics

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.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Iran, Islamic Republic of 2 3%
United States 1 1%
Singapore 1 1%
Portugal 1 1%
Latvia 1 1%
Luxembourg 1 1%
United Kingdom 1 1%
France 1 1%
Germany 1 1%
Other 2 3%
Unknown 67 85%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 15 19%
Student > Ph. D. Student 13 16%
Student > Master 13 16%
Professor 8 10%
Professor > Associate Professor 7 9%
Other 15 19%
Unknown 8 10%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 26 33%
Computer Science 14 18%
Biochemistry, Genetics and Molecular Biology 7 9%
Social Sciences 5 6%
Engineering 4 5%
Other 12 15%
Unknown 11 14%
Attention Score in Context

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 21 June 2014.
All research outputs
#18,373,874
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#6,305
of 7,272 outputs
Outputs of similar age
#95,828
of 112,002 outputs
Outputs of similar age from BMC Bioinformatics
#81
of 93 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 5th percentile – i.e., 5% 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 112,002 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.