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Our detection method identifies an outbreak on October 10th.
This method identifies an optimal number of genetic clusters that best describe the data by running a k-means clustering algorithm and comparing the different clustering solutions using the Bayesian Information Criterion BICC).
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The method identifies a low order process model with a quantification of model errors (uncertainty).
This method identifies a fault section by measuring as to which section possesses the minimum voltage level.
The second method identifies a model for which the Pareto point is the closest (based on Euclidean distance) to a centroid of all points in the Pareto neighbourhood.
Finally, our method identifies a module including RB1 and a metagene DKK1/PRKG1/CSTF2T significant at p1 <0.001 and p2 = 0.02 levels.
Review was both explicit (criterion based) and implicit (holistic) because each method identifies a different spectrum of errors.
Instead, the PSEUDO method identifies a range of CO2 output fluxes that are consistent with near-optimal growth.
Specifically, the sensitivity-based method identifies a higher number of true positives than the fitness-based method at any false-positive rate.
The Peak Day method identifies a much more precise window of the periconceptional period than that used in most previous studies of periconceptional exposures.
Our method identifies a global co-regulation network, containing thousands of genes, as well as clusters of genes found within the network.
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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