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Incremental parameter estimation of kinetic metabolic network models

Overview of attention for article published in BMC Systems Biology, November 2012
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Mentioned by

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2 tweeters

Citations

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22 Dimensions

Readers on

mendeley
85 Mendeley
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3 CiteULike
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Title
Incremental parameter estimation of kinetic metabolic network models
Published in
BMC Systems Biology, November 2012
DOI 10.1186/1752-0509-6-142
Pubmed ID
Authors

Gengjie Jia, Gregory Stephanopoulos, Rudiyanto Gunawan

Abstract

An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE). Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified).

Twitter Demographics

The data shown below were collected from the profiles of 2 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Latvia 1 1%
France 1 1%
United Kingdom 1 1%
Spain 1 1%
Germany 1 1%
Unknown 78 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 31%
Researcher 19 22%
Student > Master 11 13%
Professor 9 11%
Other 4 5%
Other 10 12%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 29%
Engineering 18 21%
Biochemistry, Genetics and Molecular Biology 11 13%
Chemical Engineering 8 9%
Computer Science 7 8%
Other 9 11%
Unknown 7 8%

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 29 November 2012.
All research outputs
#12,357,523
of 18,796,975 outputs
Outputs from BMC Systems Biology
#637
of 1,127 outputs
Outputs of similar age
#164,455
of 272,701 outputs
Outputs of similar age from BMC Systems Biology
#47
of 87 outputs
Altmetric has tracked 18,796,975 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 33rd percentile – i.e., 33% 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 272,701 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.