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Mendeley readers
Attention Score in Context
Chapter title |
ODE-Based Modeling of Complex Regulatory Circuits
|
---|---|
Chapter number | 20 |
Book title |
Plant Gene Regulatory Networks
|
Published in |
Methods in molecular biology, June 2017
|
DOI | 10.1007/978-1-4939-7125-1_20 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7124-4, 978-1-4939-7125-1
|
Authors |
Seaton, Daniel D., Daniel D. Seaton |
Editors |
Kerstin Kaufmann, Bernd Mueller-Roeber |
Abstract |
Transcriptional regulatory circuits are often complex, consisting of many components and regulatory interactions. Mathematical modeling is an important tool for understanding the behavior of these circuits, and identifying gaps in our understanding of gene regulation. Ordinary differential equations (ODEs) are a commonly used formalism for constructing mathematical models of complex regulatory networks. Here, I outline the steps involved in developing, parameterizing, and testing an ODE model of a gene regulatory network. |
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 % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 6 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 33% |
Researcher | 2 | 33% |
Unknown | 2 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 33% |
Agricultural and Biological Sciences | 2 | 33% |
Unknown | 2 | 33% |
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 18 June 2017.
All research outputs
#18,555,330
of 22,981,247 outputs
Outputs from Methods in molecular biology
#7,946
of 13,149 outputs
Outputs of similar age
#241,820
of 316,926 outputs
Outputs of similar age from Methods in molecular biology
#170
of 280 outputs
Altmetric has tracked 22,981,247 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,149 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% 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 316,926 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 280 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.