Difference between revisions of "Project 20 Benchmark Problems for Modelling Intracellular Processes"
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| − | == | + | == Summary == |
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Ordinary differential equation (ODE) models are frequently applied to describe intracellular biochemical processes. | Ordinary differential equation (ODE) models are frequently applied to describe intracellular biochemical processes. | ||
Here, we provide a set of 20 published benchmark models which should serve as test cases for assessing methodology for modelling. | Here, we provide a set of 20 published benchmark models which should serve as test cases for assessing methodology for modelling. | ||
| + | An overview about the benchmark models is also provided at the github repository [https://github.com/Benchmarking-Initiative/Benchmark-Models Benchmark-Models]. | ||
| − | + | == Download == | |
| − | + | The 20 benchmark models are available with version control on github [https://github.com/Benchmarking-Initiative/Benchmark-Models Benchmark-Models]. | |
| − | The 20 benchmark models are available with version control on github [ | ||
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| + | == Studies based on the 20 Benchmark problems == | ||
Here, we summarize several outcomes of benchmark studies performed on the 20 benchmark problems. | Here, we summarize several outcomes of benchmark studies performed on the 20 benchmark problems. | ||
| − | + | === Optimization at the Log-Scale === | |
It has been shown [REF] that optimization algorithms have superior performance if parameters are optimized at the log-scale [REF]. | It has been shown [REF] that optimization algorithms have superior performance if parameters are optimized at the log-scale [REF]. | ||
| − | + | === Convexity === | |
It has been shown [REF] that the negative log-likelihood which has to be minimized for parameter estimation is more convex if the parameters are evaluated at the log-scale. It has been concluded that this fact is one reason for performance benefits of deterministic optimization algorithms if optimization is performed at the log-scale [REF]. | It has been shown [REF] that the negative log-likelihood which has to be minimized for parameter estimation is more convex if the parameters are evaluated at the log-scale. It has been concluded that this fact is one reason for performance benefits of deterministic optimization algorithms if optimization is performed at the log-scale [REF]. | ||
Latest revision as of 08:52, 10 August 2018
Contents
Summary
Ordinary differential equation (ODE) models are frequently applied to describe intracellular biochemical processes. Here, we provide a set of 20 published benchmark models which should serve as test cases for assessing methodology for modelling.
An overview about the benchmark models is also provided at the github repository Benchmark-Models.
Download
The 20 benchmark models are available with version control on github Benchmark-Models.
Studies based on the 20 Benchmark problems
Here, we summarize several outcomes of benchmark studies performed on the 20 benchmark problems.
Optimization at the Log-Scale
It has been shown [REF] that optimization algorithms have superior performance if parameters are optimized at the log-scale [REF].
Convexity
It has been shown [REF] that the negative log-likelihood which has to be minimized for parameter estimation is more convex if the parameters are evaluated at the log-scale. It has been concluded that this fact is one reason for performance benefits of deterministic optimization algorithms if optimization is performed at the log-scale [REF].
