Difference between revisions of "Performance of objective functions and optimization procedures for parameter estimation in system biology models"
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| − | + | In systems biology, relative data are a common occurrence. In ODE-based models, this is regarded by either introducing scaling parameters or data-driven normalization to bring data and simulations onto the same scale. It was shown in this article, that data-driven normalization improves optimization performance and does not aggravate non-identifiability problems compared to a scaling factor approach. Furthermore, this article reports that hybrid optimization methods which combine stochastic global and deterministic local search outperforms deterministic local gradient-based strategies. | |
=== Study outcomes === | === Study outcomes === | ||
Revision as of 12:29, 25 February 2020
Contents
1 Citation
Andrea Degasperi, Dirk Fey & Boris N. Kholodenko, Performance of objective functions and optimisation procedures for parameter estimation in system biology models, 2017, Systems Biology and Applications volume 3, Article number: 20
2 Summary
In systems biology, relative data are a common occurrence. In ODE-based models, this is regarded by either introducing scaling parameters or data-driven normalization to bring data and simulations onto the same scale. It was shown in this article, that data-driven normalization improves optimization performance and does not aggravate non-identifiability problems compared to a scaling factor approach. Furthermore, this article reports that hybrid optimization methods which combine stochastic global and deterministic local search outperforms deterministic local gradient-based strategies.
3 Study outcomes
List the paper results concerning method comparison and benchmarking:
3.1 Outcome O1
The performance of ...
Outcome O1 is presented as Figure X in the original publication.
3.2 Outcome O2
...
Outcome O2 is presented as Figure X in the original publication.
3.3 Outcome On
...
Outcome On is presented as Figure X in the original publication.
3.4 Further outcomes
If intended, you can add further outcomes here.
4 Study design and evidence level
4.1 General aspects
You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:
4.2 Design for Outcome O1
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
4.3 Design for Outcome O2
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
...
4.4 Design for Outcome O
- The outcome was generated for ...
- Configuration parameters were chosen ...
- ...
5 Further comments and aspects
6 References
The list of cited or related literature is placed here.
