The aim of this study is to develop a clinically appropriate outcome prediction tool based on measurable predictors affecting outcome.
Numerical predictions for measurable properties, including the temporal evolution of the residual mass and the final char yield, are validated through comparison to data from experiments, and strong agreement is found.
This formulation equips the normative account (i.e., minimizing variational free energy or maximizing expected value) with a biologically plausible implementation an implementation that makes some clear predictions about measurable behavioral and neuronal responses.
We conclude that, for Jitterbug, genome size and complexity has no measurable effect on TEI prediction accuracy in unique regions of the genome, that heterozygous TEIs can be readily detected and zygosity of TEIs can be accurately predicted given adequate coverage.
This means that the "perfect" model will not only be able to give accurate predictions of the measurable system output, but also will provide an accurate description of all the components and processes involved in the generation of this output.
As a consequence, the number of correct predictions of the measurable response (true positive (TP) cases identified by means of the test depends on the threshold level selected.
Although simultaneous parameter fitting and full parameter identifiability could not be accomplished in this context, the updated model fulfilled the Agoram 25 criteria: (i) Fit for purpose: only compartments and parameters that were necessary to improve predictions of specific measurable clinical endpoints were added to the model.
Strategies for invasion containment and its prediction based on measurable parameters are analysed.
The estimated kinetic parameters provide a successful prediction of various measurable chemical species.
