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The model was first calibrated with control irrigation plus 150 kg N ha−1 treatment (I1N3), then evaluated (2012) and validated (2013) by utilizing experimental data.
Estimation of kinetic parameters is an important and difficult step in the development of models, but calculations of unknown kinetic parameters can be achieved by utilizing experimental data and model-based technique.
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As discussed above, utilizing experimental data is essential to extract relevant biological conclusions regarding the particular scenario being modeled.
In this paper, we adapt the model to utilize experimental data by including information on different drug concentrations.
To train the neural network of the TCC, this research utilized experimental data reported by previous researchers from the 1980's to the 2000's.
The model will utilize experimental data on the types of mutants that arise in the context of different treatments.
Moreover, for the TF binding site predictions, we utilized experimental data retrieved from ChIP-Seq experiments.
The constructed intelligent models are evaluated by utilizing extensive experimental data reported in open literature.
The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data.
The same study showed that it was possible to reduce the number of test animals significantly by utilizing existing experimental data in conjunction with (Quantitative) Structure Activity Relationship ((Q SAR) models.
FINDSITEX utilizes experimental binding data without ligand bound experimental structures.
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