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The mixture was fitted using an EM algorithm [ 11] with initial parameters calculated from the clusters returned by k-means with centers set to the eight expected locations of the copy number groups.
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The adsorption kinetics and isotherms of the optimised mixture were fit by the pseudo-first order kinetic model and Langmuir isotherm.
Secondly, Normal mixtures is fitted directly using the SR method via mclust package (Fraley and Raftery 2002, 2007) in R.
Two-component normal mixtures were fit to the data to identify bimodal genes and their potential roles in cell signaling and disease progression.
A multivariate normal mixture model was fitted to the data, imposing spherical clusters with identical variance, and allowing for up to 20 mixture components.
A binormal mixture model was fitted to the log-transformed optical density values for wild boar up to 20 months old to estimate the optimal cut-off value (−0.685) and accompanying sensitivity (90.6%) and specificity (93.6%).
We first flattened the background noise using Metamorph, and then a two-component Gaussian mixture model was fitted to the pixel intensity distribution of each image using the Expectation Maximation algorithm.
Following [ 35] a two-component normal mixture model was fitted to identify DE/DPIF genes.
Therefore, a finite mixture model was fitted to identify classes of individuals following similar patterns of behaviour over time.
Since information about the NAT2 genotypes of participants was not available, a bivariate normal mixture model was fitted to the distribution of the half-lives to estimate the proportion of acetylator phenotypes from the data.
The mixture model was fitted using a combination of a Newton-type method and the expectation-maximization (EM) algorithm (16) (the normalmixEM2comp function from the Mixtools package in R).
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