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I understand the allure of predictions, but these models have well-known flaws.
All OMIM-based predictions had higher p-values than CTD-based predictions, but these differences should be interpreted in light of the fewer known proteins in the OMIM database associated with the diseases predicted.
This method is not perfect due to the potential for error in our predictions, but these areas will be clarified as the edited sequences are obtained at the bench.
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Finite volume computational fluid dynamics (CFD) codes can assist with these predictions, but this approach is computationally expensive.
Our data do not contradict these predictions but the deceleration in the rate of increase suggests that there will be smaller increases than they forecasted.
We do not have access to experimentally test these predictions, but this manuscript may lead to others doing this work perhaps.
Genome-wide measurement of epigenetic features, such as histone modifications and occupancy by transcription factors, is improving enhancer predictions, but the contribution of these features to prediction accuracy is not known.
There has been less use in support of clinical decision-making and prediction, but these applications are increasing [ 11, 12].
The approaches are ideally informed by detailed model predictions but ultimately these are empirical questions that have to be tested experimentally.
Treated children grow, on average, one to two inches taller than predicted — but these predictions are inexact, and the growth for each individual child unknown.
In fact, two target sites in Abd-A were predicted to be located in the loop regions when only 3′ UTR was used for the prediction, but the locations of these sites changed to the stem regions when entire mRNA was considered (supplementary table S3, Supplementary Material online).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com