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In the case of agent countermeasures, PBPK/PD modeling also allows the prediction of therapeutic efficacy under varying exposure scenarios.
Modeling also allows us to estimate that the total number of initial quantum dots in aggregates at the beginning of the fluorescence-quenching phenomenon is approximately 200.
Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference.
In contrast to these mutations that led to a decrease in the specificity of the ERK2-Ets interaction, the modeling also allows for a prediction of potential mutants that might improve specificity.
Multilevel modeling also allows for the use of unbalanced data.
By way of extrapolation, regression modeling also allows predictions about effects in dose ranges that were not actually tested.
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Using an ANN modeling also allowed us to determine the inputs (nucleotide positions) that have the most significant effect on the output (disease outcome).
It is noteworthy to mention that many software packages for in silico metabolic modeling also allow users to visualize and analyze molecular pathways and networks with -omics data.
The model also allows for squeeze-film damping effects.
Furthermore, a model also allows the meteorological fields associated with the extreme events to be extracted.
A dynamical model also allows investigation of the mechanisms of extreme events and remote precursors, which can aid prediction.
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