Distribution of all models in terms of the pre-defined criteria. by Ammara Gull E. Fareen (11973606)

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Performance statistics for the best models selected based on pre-defined criteria. by Ammara Gull E. Fareen (11973606)

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Fig 4 - by Ammara Gull E. Fareen (11973606)

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General statistics of models that met distinct criteria. by Ammara Gull E. Fareen (11973606)

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Jackknife of regularized training gain for <i>Brachyponera nigrita</i>. by Ammara Gull E. Fareen (11973606)

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<i>Brachyponera nigrita</i> (worker). by Ammara Gull E. Fareen (11973606)

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Future predicted distribution (RCP 4.5, 2050) of <i>Brachyponera nigrita</i> by Maxent model. by Ammara Gull E. Fareen (11973606)

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Predicted distribution of <i>Brachyponera nigrita</i> in calibration area by Maxent model. by Ammara Gull E. Fareen (11973606)

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Correlation matrix of predictor variables. by Ammara Gull E. Fareen (11973606)

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Correlation values between state and national models when predicting state-level suitability probabilities across species and model types. by Alyson L. Singleton (16844622)

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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>…”

Characteristics and outcomes of patients who received and did not received massive blood transfusion. by Shao-Chun Wu (478105)

Myocardial native T₁ maps. by Hossam El-Rewaidy (6203378)

Predictive Models for Human Organ Toxicity Based on <i>In Vitro</i> Bioactivity Data and Chemical Structure by Tuan Xu (338730)

“…The top four models, with AUC-ROC values >0.8, were derived for endocrine (0.90 ± 0.00), musculoskeletal (0.88 ± 0.02), peripheral nerve and sensation (0.85 ± 0.01), and brain and coverings (0.83 ± 0.02) toxicities, whereas the best model AUC-ROC values were >0.7 for the remaining 10 toxicities. …”

Proportion of Feature Importance. by Chunliang Jiang (22477803)

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Plasma Proteomic Profiling Yields a High-Performance Biomarker Panel for Predicting a Poor Prognosis in Patients with COVID-19 by Zhijin Zhang (204027)

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Identification results of four algorithms on test sets for different feature subsets. by Chunliang Jiang (22477803)

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Definitions of mortality prediction for patients with hypothermia. by Chunliang Jiang (22477803)

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Metrics used to evaluate the prediction performance. by Chunliang Jiang (22477803)

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Feature selection results for (A) Random Forest, (B) Logistic Regression, (C) XGBoost, and (D) Naïve Bayes. by Chunliang Jiang (22477803)

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