Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients by Wenchao Zhou (8086469)

“…These findings provide a direction for further studies on the molecular mechanisms underlying COVID-19.…”

VIF values for random forest prediction models. by Yang Xu (178421)

“…Using logistic regression, LASSO regression, and random forest (RF) algorithms, we constructed nine prediction models, evaluating their performance via AUROC, sensitivity, specificity, Youden’s index, decision curve analysis (DCA), and calibration curves.…”

Predictors included in the models. by Yang Xu (178421)

“…Using logistic regression, LASSO regression, and random forest (RF) algorithms, we constructed nine prediction models, evaluating their performance via AUROC, sensitivity, specificity, Youden’s index, decision curve analysis (DCA), and calibration curves.…”

VIF values for lasso prediction models. by Yang Xu (178421)

“…Using logistic regression, LASSO regression, and random forest (RF) algorithms, we constructed nine prediction models, evaluating their performance via AUROC, sensitivity, specificity, Youden’s index, decision curve analysis (DCA), and calibration curves.…”

Machine Learning-Driven Methods for Nanobody Affinity Prediction by Hua Feng (234718)

“…In summary, the current study provides, for the first time, a tool that can effectively predict whether there is an affinity between nanobodies and their intended ligands and explores the key factors that influence their affinity, which could improve the screening and design process of Nbs and accelerate the development of Nb drugs and applications.…”

VIF values for logistic prediction models. by Yang Xu (178421)

“…Using logistic regression, LASSO regression, and random forest (RF) algorithms, we constructed nine prediction models, evaluating their performance via AUROC, sensitivity, specificity, Youden’s index, decision curve analysis (DCA), and calibration curves.…”

Clinical Characteristics of Patients. by Yifei Wang (95207)

“…</p><p>Conclusion</p><p>We developed a prediction model based on the optimal machine learning, XGBoost, which can assist clinical decision-making and potentially extend the survival of patients with rectosigmoid junction cancer.…”

Abbreviations used in the text. by Lorenzo Ruinelli (13014138)

“…Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated.…”

Patient screening information for this study. by Yifei Wang (95207)

“…</p><p>Conclusion</p><p>We developed a prediction model based on the optimal machine learning, XGBoost, which can assist clinical decision-making and potentially extend the survival of patients with rectosigmoid junction cancer.…”

Data. by Adriana Robles-Cabrera (2156641)

“…However, in regression models adjusted for age and glucose levels, only estradiol was found to be significant, and should be considered an important variable related to cardiovascular and autonomic balance in T2DM women and may provide crucial information to improve cardiovascular risk algorithms.</p></div>…”

Mechanisms of color change and smart control strategies during carrot drying by Qing Sun (492552)

“…To enable nondestructive, real-time monitoring, a hybrid detection system integrating near-infrared spectroscopy (NIR) and low-field nuclear magnetic resonance (LF-NMR) was developed. …”

Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mell... by Zhijun Bu (18544339)

“…<p>Predicting the prognosis of patients with acute myocardial infarction (AMI) combined with diabetes mellitus (DM) is crucial due to high in-hospital mortality rates. This study aims to develop and validate a mortality risk prediction model for these patients by interpretable machine learning (ML) methods.…”