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

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

“…After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. …”

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

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AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, 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 tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, 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 tre</p>…”

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

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dLDS attractor recovery also benefits from increasing prediction delay. by Rodrigo Osuna-Orozco (20686205)

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

Area under the ROC Curve (AUC) analysis to predict AIS conversion at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

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Area under the ROC curve (AUC) analysis to predict neurological severity at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

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<i>ΔG°</i> and C_1/2 values of various αB-crystallin samples derived from equilibrium urea unfolding analysis. by Aref Baharvand (21713886)

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Statins increased neuronal soma membrane complexity. by Shuk C. Tsoi (21192243)

TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…(C) Mutualism also promotes an increase in network connectance when introduced into assembled communities, while stopping mutualistic interactions from entering an assembled system slowly decreases it. …”

Participants produce faster wheels across generations, their understanding of the system also increases, but peaks at generation four. by Anders Högberg (3394754)

Feature importance and beeswarm plot determined by mean absolute SHAP values for the DeepSurv model. by Theophilus Gyedu Baidoo (21162511)

“…<p>(A) A comprehensive view of the influence each variable has on model predictions, with N Stage Status ranked on top (B) Bar plot of mean absolute SHAP values by decreasing order of importance</p>…”

Relationship of yield with phyllosphere fungi species richness (a), fungi species Chao1 (b), fungi species ACE (c), fungi phylogenetic diversity (d), and bacterial species richness... by Zhiming Zhong (1745548)

Over-expression of C1-FDX increased the assembly of CI and CV. by Baoyin Chen (2635399)

Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

a-b. ISG15 also increases IFN-γ production by memory-phenotype CD8+ T cells. by Yongxia Guo (8564418)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

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