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From each SNP-BN model, we extracted the SNP that was not in the set of previously identified LOAD SNPs.
With the finalized QTL model, we extracted effect sizes, explained phenotypic variance, LOD scores, and 1.8-LOD intervals for each QTL.
For each model, we extracted the variable weights of the expression probes, ranked these variable weights and selected the 5% highest and 5% lowest ranked expression probes.
From the promoter model we extracted the motif in Figure 3A, which could be used to predict promoters of genes strongly bound by MSL.
When the authors reported results from more than one multivariate model, we extracted data deriving from either the model that included the maximum number of covariates, or the model that included severity of illness and FiO2.
In order to strike a balance between simplicity and complexity of our simulation model, we extracted the β-oxidation part of the mitochondrial model (reducing complexity), modified and completed it with respect to simulating mitochondrial fatty acid oxidation deficiencies.
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For each 3D rock model, we extract the pore system and convert this into a network representation that allows flow properties to be calculated.
Interpreting the results with the OWRK model we extract the polar and disperse surface tension components from which we deduced typical evolution of the surface properties with the different treatment settings.
By fitting the new frame with the Gaussian model, we extract the background image.
After training the body-based feature learning model, we extract f as the body-based feature for each pedestrian image.
For the purpose of this model, we extract only the adjectives, nouns and the verbs from the tagged sentence by eliminating the stop words from the query.
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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