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Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

Overview of attention for article published in BMC Systems Biology, February 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#39 of 1,132)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
twitter
8 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
61 Dimensions

Readers on

mendeley
159 Mendeley
citeulike
7 CiteULike
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Title
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
Published in
BMC Systems Biology, February 2014
DOI 10.1186/1752-0509-8-13
Pubmed ID
Authors

Pablo Meyer, Thomas Cokelaer, Deepak Chandran, Kyung Hyuk Kim, Po-Ru Loh, George Tucker, Mark Lipson, Bonnie Berger, Clemens Kreutz, Andreas Raue, Bernhard Steiert, Jens Timmer, Erhan Bilal, Herbert M Sauro, Gustavo Stolovitzky, Julio Saez-Rodriguez

Abstract

Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 3%
United States 4 3%
Germany 3 2%
Spain 2 1%
Portugal 1 <1%
Unknown 145 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 54 34%
Student > Ph. D. Student 40 25%
Student > Master 13 8%
Professor > Associate Professor 9 6%
Professor 8 5%
Other 27 17%
Unknown 8 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 37%
Biochemistry, Genetics and Molecular Biology 23 14%
Computer Science 15 9%
Mathematics 15 9%
Engineering 11 7%
Other 25 16%
Unknown 11 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 19 May 2020.
All research outputs
#2,164,998
of 25,373,627 outputs
Outputs from BMC Systems Biology
#39
of 1,132 outputs
Outputs of similar age
#24,624
of 322,489 outputs
Outputs of similar age from BMC Systems Biology
#1
of 36 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 96% of its peers.
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 322,489 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.