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. …”

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

“…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …”

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

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

Subjects:

(A) Network map of the top ten anti-AD core targets and the top 30 KEGG pathways. The color of the pathway nodes changed from pink to light pink as the DC value decreased. (B) 19 core pathways with DC values (ranked by DC>  average value of (4.933)). by Shakeel Ahmad Khan (13202394)

“…The color of the pathway nodes changed from pink to light pink as the DC value decreased. (B) 19 core pathways with DC values (ranked by DC>  average value of (4.933)).…”

Scheme of g-λ model with larger values λ. by Zhanfeng Fan (20390992)

“…And if the value of λ assumes larger values, the distortion in the shape of the transmitted wave is associated with the plastic deformation in the uncoupled rock mass. …”

Biases in larger populations. by Sander W. Keemink (21253563)

“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”

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

Subjects:

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)

Subjects:

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

Percentage of exhale flux penetrating through the mask fabric for different values of <i>c</i>_<i>k</i>; a–c and d–f decreasing porosity for outward and inward protection model respectively; <i>k</i>_<i>L</i> is head loss coefficient for inward protection model. by Akshay Anand (118579)

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>…”

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

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:

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

“…(B) Bar plot of mean absolute SHAP values by decreasing order of importance.</p>…”

Results of Analysis of Co-variance (ANCOVA) for change in parameter predicted by treatment group with pre-treatment value as a covariate. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

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