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We are motivated to develop a mean-field approach on two counts: first, if we can demonstrate close qualitative and quantitative behaviour between the mean-field and stochastic models, then it may be far simpler to implement predictions based on analytic calculations rather than running full simulations.
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pombe orthologs, suggesting that methods that implement prediction of HI based on gene properties could be crucial for identifying HI genes a priori in different species.
Previously published results support that although it is not feasibly to implement genomic prediction based on low-density genotypes (Habier et al. 2009; Cleveland et al. 2010) the accuracy of genomic evaluation is still feasible for practical implementation when genotypes in selection candidates are accurately imputed to high density (Weigel et al. 2010; Cleveland and Hickey 2013).
We focused on predictions based on input parameters that are available in continental-scale spatial layers in Europe, thus enabling users to implement the functions at this scale.
We made our predictions based on that much.
Methods for evaluating predictions based on machine learning.
Predictions based on numerical modelling are considered more appropriate, and, since debris and sediment flows exhibit similar behaviour to tailings flows, numerical models implemented for the analysis of rapid landslide run-out are suitable for the modelling of tailings dynamics.
Make predictions based on your hypothesis.
Make predictions based on the star chart.
A method of de novo TFBS prediction based on MEME was implemented, tested, and validated on a muscle-specific dataset.
We also used it for prediction based on SVMs, as implemented in the SMO option, and for prediction using Naïve Bayes classifiers.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com