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We present a method for experimental design, optimizing data acquisition for maximum confidence in the soil-plant model selection task.
The first was the understanding of the problem as a model selection task related to clustering.
Thus, in order to perform more accurately the model selection task, we also took into consideration the classification performance on the training set.
However, through the quantization steps outlined above, the entropy of all the sets of genes under consideration at any iteration is the same, thus removing any bias from the model selection task.
However, as we do not know which associations among the variables might be affected by confounding, this would lead to an enormous amount of possible models thus increasing the computational burden of the model selection task by a multiple.
More generally, the presented methodology is suitable for any ODE-based model selection task, such as the modeling of protein signaling, gene regulation, or drug processing [ 47], nowadays frequently put forward in systems biology [ 48, 49] or pharmacogenetics [ 50].
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Model selection test.
A more promising and efficient direction would be to consider model selection at the task level where only the most relevant and useful tasks are used for multitask learning.
Model selection is then the task of determining if a particular model structure is a good description of certain experimental data.
Few computational tools that address the structure identification task (e.g., ABC-SysBio) recast model selection into a parameter estimation task [ 21].
Among others, models for the Wason selection task or the suppression task are discussed by psychologists and cognitive scientists.
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model selection functionality
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model validation task
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