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Predictive models successfully classified constitutive polyA sites from a biologically relevant background (auROC = 99.6%), as well as tissue-specific regulated sets from each other.
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The model successfully classified 84% of the participants (table 2).
The model successfully classified 85% of urban girls (table 3).
At the final step, the model successfully classified 85% of the participants (table 2).
The cross-validation analysis showed that the original predictive model successfully classified patients by their risk of neutropenic event.
In the cross-validation analysis, the original model successfully classified patients by risk of neutropenic events (C = 0.78).
This model successfully classified 89% of patients with 89.3% sensitivity and 87.5% specificity, 96.3% PPV, and 87.5% NPV for BOO [AUC: 0.96].
The proposed framework successfully classified the changes to glycosylation profiles and segregated HIV disease groups.
The critical issue we address here is whether data generated by one or the other nested model are successfully classified as such in a fit of the full model in which both mechanisms are free to vary.
In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA).
The results demonstrate that the proposed matrix-based model can successfully classify positive and negative samples in all five activation functions that we investigated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com