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

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

“…<p>Using the invasion model, we investigate the effect of switching on and off (black vs grey) invasions with mutualisms halfway through the simulation (i.e. after 500 assembly events). …”

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. …”

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

Participants characteristics. by Sakiko Fukui (387048)

Subjects:

A flow diagram of the study entry. by Sakiko Fukui (387048)

Subjects:

Supplementary Material for: Longitudinal Decrease in Left Ventricular Size with Age: Impact on Mortality and Cardiovascular Hospitalization by figshare admin karger (2628495)

“…This study investigated clinical characteristics and outcomes associated with longitudinal decreases in LV size in this population. Methods: We analyzed echocardiographic data from 6,232 adults with normal baseline left ventricular end-diastolic diameter (LVEDD), with a mean interval of 4.8 years between baseline and follow-up echocardiograms. …”

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>…”

Overview of the six original ACTIV-3/TICO trials. by Elizabeth S. Munroe (21568166)

Subjects:

Significant Decrease in Vessel Count of Trade Vessels in (a) Bosporus Strait and (b) Bab el-Mandeb Strait. by Cherryl Chico (20978829)

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

“…<p>(A) SHAP value distribution for features, highlighting the relationship between feature values and their impact on model predictions. (B) Bar plot of mean absolute SHAP values by decreasing order of importance.…”

Baseline patient characteristics. by Oscar F. C. van den Bosch (22184246)

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Supplementary Material for: Priming short children with sex steroids prior to growth hormone testing decreases the frequency of divergent results by figshare admin karger (2628495)

Decreased production of antiviral factors in patients infected with SARS-CoV-2 due to reduced TLR7 expression by Julia Cataldo Lima (22811497)

“…</p> <p>Results showed that infected individuals had reduced baseline expression of antiviral factors and a diminished response to stimulation with a TLR7/8 agonist (CL097), compared to healthy individuals. A decrease in pattern recognition receptors (PRRs) was also observed at both the gene and protein levels. …”

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)

Subjects:

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

Subjects:

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)

“…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>…”

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

K-means++ clustering algorithm. by Zhen Zhao (159931)

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CM-DWM-EC chart using 30% decrease in the mean for the E(KeV). by Shumaila Nisar (20114256)

“…<p>CM-DWM-EC chart using 30% decrease in the mean for the E(KeV).</p>…”