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We also built a classifier based on the adjective check-list scale (ACL) because this scale would be the most feasible one to be used in clinical setting.
We quantified the strength of the association and then built a classifier based on multi-variates Cox regression.
And then we built a classifier based on the selected features and applied the classifier to predict breast cancer from peripheral blood in an independent testing set.
We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age.
This study built a classifier based on grey matter in the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes, which reached an accuracy around 90% in discriminating between the ASD and control groups.
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We leverage the insights from this study to build a classifier based purely on static features to identify 92% of the remaining malicious singletons at a 1.4%percentt false positive rate, despite heavy use of obfuscation and packing techniques by most malicious singleton files that we make no attempt to de-obfuscate.
PAM builds a classifier based on a ranking of CpG loci using a penalized t-statistic and then determines the misclassification error rate through 10-fold cross-validation.
Our goals are first to identify distinguishing features for such urine excretory proteins and then to build a classifier based on these features to predict which proteins in cells can be excreted into urine.
The most frequently occurring value of parameter λ during the cross validation was used to build a classifier based on all training samples for independent testing.
Typically the analysis system will characterise the morphological features as well as the enhancement kinetics of the lesion (either using automated or manual lesion segmentation), then build a classifier based on those features that yield the highest diagnostic performance [ 27– 29].
We have therefore built a prognostic classifier based on the genes from the MammaPrint signature that is as accurate in predicting 5-year disease-free survival as the MammaPrint prognostic test based.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com