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Generic modeling parameters are established for the STM procedure, enabling the models to be adapted to new repair applications.
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The combined model also includes a term enabling the model to react inversely to large deviations between the actual values and forecast.
When combined with an optimization step, it is possible to estimate model parameters, enabling the model to fit dynamics observed in data.
Phylogenetic information could also be used to inform future iterations of the species distribution models used here by enabling the model to characterize and map environmental suitability for different vector subspecies.
With its full probabilistic model, various emission densities are provided, enabling the model to handle normally distributed data from traditional array platforms as well as counting data from sequencing experiments.
We include parameter values that match realistic values enabling the model to describe processes of both dynamic and stable epigenetic pattern formation on both local chromatin sites and larger more widespread chromatin regions.
Good estimations of parameters and initial state variables are required to enable the models to make accurate estimations.
Hence PCM techniques enable the models to extrapolate - within limits imposed by the data sets, descriptors, and modeling method - in both the chemical domain (to related ligands), and the biological domain (to related targets).
Supplying quantitative information to the training sets will likely enable the models to make more accurate predictions.
Adopting SBML format enables the models to be easily used as the base of computational data analysis or simulation of dynamic behaviors.
This improved component enables the model to predict uniaxial ratcheting with more accuracy.
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