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However, when training our models, we required only that the chosen genes/probes be robustly and reproducibly predictive of relapse.
To ensure convergence for more complex models, we required iterations of up to 400 000 burn-in and 400 000 sample size.
To avoid overfitting our models, we required at least 10 observations per variable term for our Cox regression model, for a total of 190 disabled workers.
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For the compound heterozygous model, we required two coding variants in the same gene, one inherited from each heterozygous parent.
In order to ensure high reliability of the patterns used in the demonstration model, we restricted our search to patterns of prevalence at least 15% (for the enhanced model we required the prevalences to be at least 20%).
In designing the model, we require that cooperating applications communicate with one another through JAVA RMI (remote method invocation).
In order to have a well-defined model, we require initial conditions and boundary conditions.
In the previous model, we require that the target genes and the query gene share similar expression levels in selected experimental conditions.
In our model, we require only a single error term for all neurons; this is in strong contrast to error back-propagation, which requires the computation of a large number of error terms, i.e., as many error terms as the output neurons.
To estimate fitness in the equilibrium quasispecies model, we require a sample of the viral population.
To express the probability density of a structured tree under this model, we require the following additional definitions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com