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Efficiently gap-filling reaction networks

Overview of attention for article published in BMC Bioinformatics, June 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (58th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
78 Mendeley
citeulike
2 CiteULike
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Title
Efficiently gap-filling reaction networks
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-225
Pubmed ID
Authors

Mario Latendresse

Abstract

Flux Balance Analysis (FBA) is a genome-scale computational technique for modeling the steady-state fluxes of an organism's reaction network. When the organism's reaction network needs to be completed to obtain growth using FBA, without relying on the genome, the completion process is called reaction gap-filling. Currently, computational techniques used to gap-fill a reaction network compute the minimum set of reactions using Mixed-Integer Linear Programming (MILP). Depending on the number of candidate reactions used to complete the model, MILP can be computationally demanding.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 1%
Spain 1 1%
United States 1 1%
Colombia 1 1%
Unknown 74 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 26%
Researcher 12 15%
Student > Master 9 12%
Student > Bachelor 6 8%
Professor 4 5%
Other 14 18%
Unknown 13 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 26%
Biochemistry, Genetics and Molecular Biology 19 24%
Engineering 7 9%
Computer Science 7 9%
Environmental Science 2 3%
Other 6 8%
Unknown 17 22%

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 01 July 2014.
All research outputs
#7,866,600
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#2,959
of 5,420 outputs
Outputs of similar age
#74,599
of 188,460 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 3 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 42nd percentile – i.e., 42% 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 188,460 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.