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The nomogram has been validated in independent samples with predictive accuracy (Harrell's concordance index: 0.79 to 0.81; area under the curve (AUC): 0.89).
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Using as few as 100 genes, we were able to classify ETOP samples as being similar to DOX treated samples with 100% predictive accuracy.
This method enables estimation of parameters in small samples with strongly predictive covariates, where the phenomenon of separation makes at least one parameter estimate diverge to ± infinity.
In previous work we demonstrated that gene component analysis could discriminate estrogen receptor (ER) positive and negative breast cancers and gene component classifiers could be projected into independent samples with high predictive accuracy, as well as an integrated step of automatic gene selection [ 8].
Accurate mass and retention time methods aligned samples to generate quantitative peptide data, with predictive modeling using Bayesian sparse latent factor regression.
The reproducibility of this data was high with the second set of samples being predictive of the first set.
The proportion of confirmed positive samples (positive predictive value) varied from 50 to 100%, with an average of 91.71%.
A total of 6 probe sets with predictive value were isolated using PAM analysis to separate ALL from AML samples with and without the presence of MLL aberration.
Results also differ across participant age, with social deficits being more predictive of death in samples with an average age younger than 65 years.
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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