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We have made a number of assumptions in the model choices we have made that could have influenced our results.
Note that the conserving of genome ends and differentiation of middle genome regions may be the result of specific model choices we made.
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Concerning the model choice, we find that good on-site performance coincides with good transfer performance.
For model choice, we used the Deviance Information Criterion DICC) developed as a measure of fit and model complexity.
For model choice we use difference in deviance information criteria (DIC).
To explore the extent to which biological conclusions are influenced by model choice, we compared the expected ancestral domain content and the expected events predicted by the C, L, FL2, and FL3 models.
To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample.
Because the uncertainties around the resultant curve do not reflect the uncertainty in model choice, we also used model averaging as an alternative approach, where multiple models are fit explicitly and averaged, weighted by their probability of being correct given the data.
To investigate the nature of eQTL hotspots and the influence of model choice, we chose nine genes that were reported by Chesler et al. [ 12] as influenced by the largest trans regulatory QTL hotspot, i.e., that close to marker D6Mit150 on murine chromosome 6.
In order to allow readers to comprehend the consequences of these specific modeling choices we discuss effects of what we consider straightforward alternative modeling assumptions below.
These are all modeling choices we made in an effort to adopt biologically reasonable assumptions.
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