Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery by Steffen Dietenberger, Marlin M. Mueller, Andreas Henkel, Clémence Dubois, Christian Thiel, Sören Hese

“…By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. …”
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Efficacy of TIPS plus extrahepatic collateral embolisation in real-world data: a validation study by Chao Chen, Feng Zhang, Hao Wu, Wei Wu, Hui Xue, Yuzheng Zhuge, Chunqing Zhang, Guangchuan Wang, Zhengjie Li, Qiong Wu, Bo Wei, Jun Tie, Xiubin Sun, Lianhui Zhao, Yifu Xia, Jiao Xu, Peijie Li

“…Additionally, the random forest models showed higher accuracy and AUROC comparing to other models. …”
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Deep-learning-based extraction of circle of Willis topology with anatomical priors by Dieuwertje Alblas, Iris N. Vos, Micha M. Lipplaa, Christoph Brune, Irene C. van der Schaaf, Mireille R. E. Velthuis, Birgitta K. Velthuis, Hugo J. Kuijf, Ynte M. Ruigrok, Jelmer M. Wolterink

“…Subsequently, a binary Random Forest classifier labels each artery as normal or hypo-/aplastic. …”
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Adjuvant Therapy after Esophagectomy for Esophageal Cancer: Who Needs It?: Multi-institution Worldwide Observational Study by Siva Raja, MD, PhD, Thomas W. Rice, MD, Min Lu, PhD, Marie E. Semple, MPH, Andrew J. Toth, MS, Eugene H. Blackstone, MD, Sudish C. Murthy, MD, PhD, Usman Ahmad, MD, Michael McNamara, MD, Hemant Ishwaran, PhD, for the Worldwide Esophageal Cancer Collaboration Investigators

“…From 1970 to 2014, 22,123 consecutive patients from 33 centers on 6 continents (Worldwide Esophageal Cancer Collaboration) were diagnosed with biopsy-proven adenocarcinoma (n = 7526) or squamous cell carcinoma (n = 5625), of whom 10,873 received esophagectomy alone and 2278 additional adjuvant therapy. Random forests for survival and virtual-twin analyses were performed for all-cause mortality. …”
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Neutrophil to Lymphocyte Ratio Predicts Adverse Cardiovascular Outcome in Peritoneal Dialysis Patients Younger than 60 Years Old by Yingsi Zeng, Zijun Chen, Qinkai Chen, Xiaojiang Zhan, Haibo Long, Fenfen Peng, Fengping Zhang, Xiaoran Feng, Qian Zhou, Lingling Liu, Xuan Peng, Evergreen Tree Nephrology Association, Guanhua Guo, Yujing Zhang, Zebin Wang, Yueqiang Wen, Jiao Li, Jianbo Liang

“…Kaplan-Meier cumulative incidence curve and multivariable COX regression analysis were used to determine the relationship between NLR and the incidence of adverse CV outcome, while a competitive risk model was applied to assess the effects of other outcomes on adverse CV prognosis. Besides, forest plot was investigated to analyze the adverse CV prognosis in different subgroups. …”
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Temperature Is a Key Factor Affecting Total Phosphorus and Total Nitrogen Concentrations in Northeastern Lakes Based on Sentinel-2 Images and Machine Learning Methods by Haoming Qin, Chong Fang, Ge Liu, Kaishan Song, Zhuoshi Li, Sijia Li, Hui Tao, Zhaojiang Yan

“…Results indicate that random forest (RF) and XGBoost regression algorithms perform better. …”
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Diagnostic Accuracy of Procalcitonin for Bacterial Infection in Liver Failure: A Meta-Analysis by Xinchun He, Liang Chen, Haiou Chen, Yuqing Feng, Baining Zhu, Caixia Yang

“…In addition, the threshold effect analysis showed that the threshold effect was 0.23 and the correlation coefficient was −0.48, indicating that there was no threshold effect. In the forest map, the DOR of each study and the combined DOR are not distributed along the same line, and Q = 2.2 × 1014, P≤0.001. …”
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Deep learning captures the effect of epistasis in multifactorial diseases by Vladislav Perelygin, Alexey Kamelin, Alexey Kamelin, Nikita Syzrantsev, Layal Shaheen, Layal Shaheen, Anna Kim, Nikolay Plotnikov, Anna Ilinskaya, Valery Ilinsky, Alexander Rakitko, Alexander Rakitko, Maria Poptsova

“…For machine learning methods we used multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), Lasso regression, random forest and gradient boosting models. Performance of machine learning models were assessed using accuracy, AUC-ROC, AUC-PR, recall, precision, and F1 score.ResultsFirst, we tested ensemble tree methods and deep learning neural networks against LASSO linear regression model on simulated data with different types and strength of epistasis. …”
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Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning by Huai Huang, Guangqin Ren, Shanghui Sun, Zhi Li, Yongtian Zheng, Lijuan Dong, Shaoliang Zhu, Xiaosheng Zhu, Wenyu Jiang

“…ObjectiveThis study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patients experiencing cardiac arrest.MethodsUtilizing data from 748 cardiac arrest patients in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 database, machine learning algorithms, including the Boruta feature selection method, random forest modeling, and SHAP value analysis, were applied to identify significant prognostic biomarkers. …”
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Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes by Fang Wang, Yukang Mao, Jinyu Sun, Jiaming Yang, Li Xiao, Qingxia Huang, Chenchen Wei, Zhongshan Gou, Kerui Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithm were applied to identify key mortality-related dietary factors, which were subsequently incorporated into risk prediction nomogram models. …”
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Predictive models for the implementation of targeted reproductive management in multiparous cows on automatic milking systems by Fergus P. Hannon, Martin J. Green, Luke O'Grady, Chris Hudson, Anneke Gouw, Laura V. Randall

“…Using data derived solely from the AMS (RBT dataset) a binary random forest classification model was constructed for both outcomes of interest. …”
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Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study by Guangyu Lu, Zhixia Su, Xiaoping Yu, Yuhang He, Taining Sha, Kai Yan, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Weijuan Gong

“…The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611–0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974–1.00). …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. NPC patients with high-risk scores experienced notably shorter progression-free survival in comparison to those with low-risk scores. …”
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EL ROL DE LOS ACTORES SOCIALES EN LA CONFIGURACIÓN TERRITORIAL DE MOQUEHUE by Celia Viviana Torrens, Elsie Marcela Jurio, Vanesa Yanina Cappelletti, Germán Gabriel Pérez, Daiana Elcira Cara Plá, Gimena Nathalie Cuevas, María Silvina Hauw, Pablo Alejandro Leyes

“…It aroses in the 40's, as a product of the development of cattle raising and forestation. Since these activities began, the permanent settlement and first commercial activities appeared, in order to cover the new demands. …”
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Conservation of wild western honey bees Apis Mellifera in the Polissia natural zone of Ukraine: history, sources of nectar and pollen by Sichenko O., Kryvyi M., Horchanok A., Kuzmenko O., Tytariova O.

“…Pollen of Potentilla erecta L, Frangula alnus L, Lamium purpureum L, as species with the longest flowering season, remain available almost throughout the honey collection season, and the anemophilic pollen-producing tree of the Fagaceae family Quercus robur L is a common source of pollen for honey bees in Polissia forests.…”
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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…A two-layer ensemble model is built to quantitatively estimate leaf nitrogen using the stacking ensemble learning (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Boosting Decision Trees (GBDT), Linear Regression (LR), and Extremely Randomized Trees (ERT) are among the basis estimators that are integrated in the first layer. …”
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Continuous Speech-Based Fatigue Detection and Transition State Prediction for Air Traffic Controllers by Susmitha Vekkot, Surya Teja Chavali, Charan Tej Kandavalli, Rama Sai Abhishek Podila, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi

“…The evaluation was carried out using various learning algorithms such as XGBoost, Adaboost, Random Forest, HistogramGB, and 1D-CNN. The ensemble algorithms demonstrated the best performance, achieving a maximum accuracy of 100% on the XGBoost test set. …”
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Functional role of termites as soil engineers: Their influence on soil organic carbon and nitrogen in pine and mahogany stands Alas Bromo by Febriani Sri Rezeki, Ariyanto Dwi Priyo, Cahyono Ongko, Dewi Widyatmani Sih, Sumani Sumani, Komariah Komariah, Tarmadi Didi, Setiawan Khoirul Himmi, Wikantyoso Bramantyo, Zaki Muhamad Khoiru

“…Alas Bromo University Forest has various stands that can influence the presence of soil macrofauna, one of which is termites. …”
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Wear Behavior Analysis and Gated Recurrent Unit Neural Network Prediction of Coefficient of Friction in Al10Cu-B₄C Composites by Mihail Kolev, Ludmil Drenchev, Veselin Petkov, Rositza Dimitrova, Krasimir Kolev, Boris Yanachkov

“…Additionally, feature importance analysis using Random Forest models identified reinforcement-related features as the dominant predictors for both COF and mass wear. …”
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Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma by Zihao Sun, Mengfei Hu, Xiaoning Huang, Minghan Song, Xiujing Chen, Jiaxin Bei, Yiguang Lin, Size Chen

“…Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. …”
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