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Efficiently gap-filling reaction networks

Overview of attention for article published in BMC Bioinformatics, June 2014
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Mentioned by

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5 tweeters

Citations

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31 Dimensions

Readers on

mendeley
80 Mendeley
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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 80 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 76 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 25%
Researcher 13 16%
Student > Master 9 11%
Student > Bachelor 6 8%
Professor 4 5%
Other 14 18%
Unknown 14 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 26%
Agricultural and Biological Sciences 19 24%
Computer Science 7 9%
Engineering 7 9%
Environmental Science 2 3%
Other 5 6%
Unknown 19 24%

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
#13,053,735
of 21,340,745 outputs
Outputs from BMC Bioinformatics
#4,266
of 6,933 outputs
Outputs of similar age
#100,574
of 202,560 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 3 outputs
Altmetric has tracked 21,340,745 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,933 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 35th percentile – i.e., 35% 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 202,560 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 3 others from the same source and published within six weeks on either side of this one.