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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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2 X users

Citations

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26 Dimensions

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86 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).

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Germany 1 1%
Latvia 1 1%
France 1 1%
Spain 1 1%
United Kingdom 1 1%
Unknown 79 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 30%
Researcher 20 23%
Student > Master 11 13%
Professor 9 10%
Other 4 5%
Other 10 12%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 30%
Engineering 18 21%
Biochemistry, Genetics and Molecular Biology 10 12%
Chemical Engineering 8 9%
Computer Science 8 9%
Other 9 10%
Unknown 7 8%
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 29 November 2012.
All research outputs
#15,256,901
of 22,687,320 outputs
Outputs from BMC Systems Biology
#644
of 1,142 outputs
Outputs of similar age
#178,203
of 275,937 outputs
Outputs of similar age from BMC Systems Biology
#28
of 52 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 32nd percentile – i.e., 32% 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 275,937 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 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.