| Title |
Functional data analysis for identifying nonlinear models of gene regulatory networks
|
|---|---|
| Published in |
BMC Genomics, December 2010
|
| DOI | 10.1186/1471-2164-11-s4-s18 |
| Pubmed ID | |
| Authors |
Georg Summer, Theodore J Perkins |
| Abstract |
A key problem in systems biology is estimating dynamical models of gene regulatory networks. Traditionally, this has been done using regression or other ad-hoc methods when the model is linear. More detailed, realistic modeling studies usually employ nonlinear dynamical models, which lead to computationally difficult parameter estimation problems. Functional data analysis methods, however, offer a means to simplify fitting by transforming the problem from one of matching modeled and observed dynamics to one of matching modeled and observed time derivatives-a regression problem, albeit a nonlinear one. |
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 % |
|---|---|---|
| Japan | 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 5% |
| Germany | 1 | 2% |
| Switzerland | 1 | 2% |
| Netherlands | 1 | 2% |
| Ireland | 1 | 2% |
| Brazil | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 36 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 36% |
| Student > Ph. D. Student | 14 | 32% |
| Student > Master | 6 | 14% |
| Professor | 4 | 9% |
| Professor > Associate Professor | 3 | 7% |
| Other | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 34% |
| Mathematics | 8 | 18% |
| Computer Science | 6 | 14% |
| Physics and Astronomy | 3 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Other | 7 | 16% |
| Unknown | 3 | 7% |
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 11 November 2014.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from BMC Genomics
#8,709
of 11,244 outputs
Outputs of similar age
#171,291
of 190,671 outputs
Outputs of similar age from BMC Genomics
#65
of 81 outputs
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