We define the term “inhibitory reduction prowess” (IRP) as the percentage by which the product formation rate is reduced by from the initial point before inhibitor is introduced to... by J. Roadnight Sheehan (20141887)

The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

Predicting pattern diversity decreases as a function of and . by Selim Haj Ali (21222613)

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

ROC Curve for the Best Model (AUC = 0.92). by Mudhafar Jalil Jassim Ghrabat (22177655)

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

AUC ROC curve. by Dieuwke Luijten (15282736)

“…Different cut-off values to define a normal VG-RVPO were evaluated. Also, we evaluated the association between mean pulmonary artery pressure (mPAP) and CMR derived indexed right ventricular (RV) mass and the VG-RVPO.…”

Increase in AUC, MCC, and F1 between oversampling and undersampling. by Seongil Han (20490232)

Decreasing oxygen saturation with corresponding levels of NLR and D-dimer for understanding the interplay of inflammation. by Narendran Gopalan (110148)

Statistical analysis of the AUC measurement. by Esra Ayan (21156015)

“…<p>Analysis indicated that INSv application significantly changed spontaneous calcium activity (IAsp <i>p</i>-value: 0.1290, INSv <i>p</i>-value: 0.0002). The mean AUC decreased in both samples.</p> <p>(TIF)</p>…”

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Performance comparison of AUC, MCC, and F1 across different IR on GMSC dataset. by Seongil Han (20490232)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

We compared the Fugl-Meyer (FMA) clinical assessment scores for individuals with stroke with their performance for each outcome measure. by Devin Sean Austin (20285144)

“…<p>FMA scores range from 0 (severe hemiparesis) to 66 (normal motor performance). We divided our individuals with stroke into subgroups based on a previously conducted cluster analysis that defined FMA > 53 as mild impairments (N = 13) and FMA < 53 as moderate to severe impairment (N = 7). …”

The AUCs of the GSRS and the established models in predicting the 1-, 2-, 3-, 4-, and 5-year OS in the TCGA-KIRC cohort. by Yu Zhang (12946)

A typical metaboliser prevalence for CYP2D6 and CYP2C19 across diverse ancestry groups. These are genetically predicted based on data published by PharmGKB and CPIC. We have define... by Emma F. Magavern (20947928)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

AUC surface with respect to regularization parameters <i>λ</i> and . by Yangsong He (13909371)

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger trends. …”