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

Comparative Internal and External AUC Performance Across Single-Site Models. by Ioana Duta (18462981)

“…Boxplots describe the distribution of AUC values obtained through bootstrap resampling, indicating the variance within internal and external validations. …”

Boxplot comparing Ct values for HD in flies and clothes. by Philippe Ndzomo (17706039)

The spike-in of synthetic hsa-lest-7a-5p miRNA decreases the Ct Values of extracted small RNA samples. by Marc Van Der Hofstadt (1423027)

CT Values of reference materials. by Guosong Jing (20840261)

Comparison of ROC AUC and PR AUC with existing models. by Guodong He (3770947)

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. …”

Comparison of ROC AUC and PR AUC with deep learning algorithms. by Guodong He (3770947)

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>…”

Predictive performance (AUC) of the UK Biobank PRS Release disease traits by ancestries. by Deborah J. Thompson (12410706)

Gaussian model fitted of the number of dogs detections of SARS-CoV-2, depending on the cycle threshold (Ct) values of RT-qPCR tests for SARS-CoV-2. by Víctor M. Vidal-Martínez (18707335)

Subjects:

Cycle threshold values at which a specific number of dogs out of 11 dogs was able to detect SARS-CoV-2 in samples from Merida, Yucatán, México. by Víctor M. Vidal-Martínez (18707335)

Subjects:

Results of a dog’s detections after exposure to sweat samples from positive and negative people to SARS-CoV-2, together with the Ct values of the RT-qPCR test of each person partic... by Víctor M. Vidal-Martínez (18707335)

Subjects:

Sensitivity and specificity of 11 dogs trained to detect SARS -CoV-2 from the sweat of positive and negative people from Mérida, Yucatán compared with the reference test (RT-qPCR).... by Víctor M. Vidal-Martínez (18707335)

Subjects:

Details of the 11 fully trained dogs exposed to sweat samples of SARS-CoV-2 positive and negative humans. by Víctor M. Vidal-Martínez (18707335)

Subjects:

Evolution of the average median AUC scores per dataset, on the validation and test sets, at increasing levels of variable selection, for taxonomic and functional (SoFA) profiles. by Baptiste Ruiz (20285217)

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

ROC AUC of CBR-KAN with existing models for 50 datasets. by Guodong He (3770947)

Demographic and ocular features. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”

Machine learning model to diagnose PCG. by Mingxi Shao (10066570)

“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H₂O₂ and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”