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Attention Score in Context
Title |
Optimal experiment design for model selection in biochemical networks
|
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Published in |
BMC Systems Biology, February 2014
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DOI | 10.1186/1752-0509-8-20 |
Pubmed ID | |
Authors |
Joep Vanlier, Christian A Tiemann, Peter AJ Hilbers, Natal AW van Riel |
Abstract |
Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 4% |
Switzerland | 1 | 1% |
Netherlands | 1 | 1% |
Malaysia | 1 | 1% |
Belgium | 1 | 1% |
Iran, Islamic Republic of | 1 | 1% |
Unknown | 83 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 27 | 29% |
Student > Ph. D. Student | 23 | 25% |
Student > Master | 11 | 12% |
Professor > Associate Professor | 7 | 8% |
Student > Bachelor | 5 | 5% |
Other | 14 | 15% |
Unknown | 5 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 19 | 21% |
Engineering | 18 | 20% |
Mathematics | 9 | 10% |
Computer Science | 8 | 9% |
Biochemistry, Genetics and Molecular Biology | 7 | 8% |
Other | 22 | 24% |
Unknown | 9 | 10% |
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 06 June 2014.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from BMC Systems Biology
#651
of 1,132 outputs
Outputs of similar age
#145,777
of 239,034 outputs
Outputs of similar age from BMC Systems Biology
#22
of 39 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 31st percentile – i.e., 31% 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 239,034 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 39 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.