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In order to further investigate the disease specificity of lipidomic changes detected in LMNA mutation carriers, we used the lipids from Table 2 to fit a logistic regression model to samples from Groups 1&2 with the LMNA mutation (elevated DCM risk) as the response variable.
To apply this model to samples containing unknown targets, we performed an exhaustive BLAST search for every probe on the array against a comprehensive database of complete microbial genome sequences.
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Most EDAs explicitly use the probabilistic model to sample new solutions without using traditional genetic operators.
Simulation studies can provide insights on the sensitivity of the mixture model to sampling decisions and characteristics of the stocks.
In order to assess the robustness of the model to sampling variation, we used data from 2008 and ran twenty additional models with 75% training and 25% test sites that were randomly selected from the original dataset.
This was addressed by constructing a model to sample alternative spindle architectures with the same polymer mass as wild-type spindles.
By identifying dishes with peptides and, respectively, customers with mass spectra, we obtain a simple model to sample peptide assignments, i.e. simulate experiments and in particular estimate the expected number of new peptide discoveries.
Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation.
An empirical distribution of T χ 2 is obtained by generating bootstrap samples from a model and fitting this model to the samples, as described above.
An empirical distribution of T dw is obtained by generating bootstrap samples from a model and fitting this model to the samples, as described above.
Prediction and Measurement: Apply a constant velocity dynamical model to the samples using Eq. (7).
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CEO of Professional Science Editing for Scientists @ prosciediting.com