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Significant deviation from the diagonal indicates an item's subgroup heterogeneity.
This iteration exhibits the largest deviation from the diagonal.
An item's subgroup heterogeneity is then indicated by significant deviation from the diagonal.
Algorithms that are good discriminators have ROC curves with strong convex deviation from the diagonal and AUCs that approach 1 and are significantly different from the AUC of the diagonal.
However, this algorithm suffered from a relatively high false positive fraction for decision thresholds near the maximum score as indicated by the weak, erratic deviation from the diagonal near the origin of the ROC curve (Fig. 5F).
Because minimizing the false positive rate is the primary concern in shRNA design, we recommend using the modified Ui-Tei et al. algorithm, which had the lowest high false positive fraction at decision thresholds near the maximum score as indicated by the strong deviation from the diagonal near the origin of the ROC curve (Figs. 5H and 5K).
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Figure 4 shows the scatterplots between the singlet and repeat proportions, deviations from the diagonal show which amino acids exhibit a preference for either being found as a singlet or as a repeat.
Deviations from the diagonal are due PCR bias and random sampling from limited template molecules.
This results in a diagonal like plot; the larger the deviations from the diagonal, the larger the prediction errors.
Small deviations from the diagonal indicate that the reward rate is indeed close-to-quadratic in these bounds.
Large deviations from the diagonal indicate large deviations from the null hypothesis and, in general, suggest a higher risk of detecting false associations [ 1].
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