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The MB_2K predictions used a BayesCπ model [ 18].
The Dutch/Flemish genomic predictions used a Bayesian mixture model for SNP effects, along with polygenic effects [ 16], assuming that most SNP had small effects and a few SNP had moderate or large effects and the French genomic predictions used a mixed linear model with a polygenic effect and random haplotype effects across the genome [ 17].
Predictions used a cut off significance p-value of 0.0001 (for dose-rate and irradiation classification) and 0.001 (for dose classification) between classes to determine the classifier gene set.
Thus, the submitted predictions used a NN with 32 inputs, corresponding to the 16 protein phosphorylation levels measured at both 5 and 25 min, a single hidden layer of 1000 logistic units and 32 logistic outputs with a cross entropy loss function.
The MB and SB predictions used a BayesC model with π set to 0.99 because the US beef industry (i.e., American Simmental Association and American Hereford Association) applied this approach to derive the genomic predictors that are included in National Cattle Evaluations.
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Our initial FBA predictions used an objective function that relates to the ability of the bacterium to convert a particular carbon source into biomass.
These analyses were based upon Latin Hypercube Sampling (LHS), a sampling technique that enables us to make predictions using a range of parameter values for each parameter.
We test our predictions using a sample of 694 innovative publicly traded firms during the years 1981-2001.
Chess is accurate because it delivers superior predictions using a combination of historical access patterns with social signals in a unified online learning framework.
We test that and other predictions using a modification of the application folders design used in double standards research.
Numerical predictions from each method are compared with available experimental data and previously reported predictions using a multigrid numerical method.
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