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Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.
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Importantly, for the scenario (accelerated 1st dose only) and the measure (RVGE hospitalizations) that are common between our study and the Halvorson et al. study, our model yielded substantially lower reductions (274 vs. 737 2210 [depending on vaccine coverage and efficacy]).
The RRPCA model yielded substantially higher accuracies for one line, which suggests that using a compact representation of the genotype data, as achieved by PCA, can indeed alleviate the severe " n < p" problem in genomic prediction, although this appears to be line-specific.
The model without any random effect did, however, yield substantially lower standard errors and affected least-squares means.
The algorithms also yield substantially fewer plausible networks.
Implementation on Irish data shows that the two approaches yield substantially different results.
The effect size and standardized response mean may yield substantially differing results.
An important consideration is whether genetic profiles yield substantially better predictive value than traditional risk factors.
Furthermore, protein-reaction pairs yield substantially worse error rates than domain-protein pairs.
The inclusion of data on greenhouse gas emissions in climate models yields a substantially better fit of observed climatic trends up to 2000.
Probit models yield similar results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com