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If the current knowledge is not sufficient, studies that incorporate mathematical models can yield predicted mechanisms that regulate gene expression to generate the phase difference, and these predictions can then be tested experimentally.
A discounted-cash-flow model that incorporates those predictions can then be used to determine the value of various alternative strategies.
Good predictions can then be associated with low values of relative accuracy.
Predictions can then be tested using transgenic, mutant or introgression/wide crossing approaches.
The current value of such predictions can then be used as an indicator for their future likelihood of occurrence.
Quantitative predictions can then be made for the maximum growth rates of localized instabilities of small amplitude, using asymptotic analysis as in geometric optics.
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The Decision Forest prediction and the k-nearest neighbor prediction can then be combined to assign the sample prediction in such a way that the expected error of the prediction is more accurate.
After constructing the IT2 fuzzy sets with the MFs and the set of rules for the infrastructure load patterns, the prediction can then start to determine service quality estimations.
Statistical inference and prediction can then be made based on the posterior distribution of these parameters.
Our prediction can then be used to obtain a "diagnostic test" for out-of-date meta-analyses.
In clinical practice, prediction can then be done without any exposure to treatment, which enables it to be restricted to responders.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com