Recent centennial drought on the Tibetan Plateau is outstanding within the past 3500 years by Yu Liu, Huiming Song, Zhisheng An, Qiang Li, Steven W. Leavitt, Ulf Büntgen, Qiufang Cai, Ruoshi Liu, Congxi Fang, Changfeng Sun, Kerstin Treydte, Meng Ren, Lidong Mo, Yi Song, Wenju Cai, Quan Zhang, Weijian Zhou, Achim Bräuning, Jussi Grießinger, Deliang Chen, Hans W. Linderholm, Ashish Sinha, Hai Cheng, Lu Wang, Ying Lei, Junyan Sun, Wei Gong, Xuxiang Li, Linlin Cui, Liang Ning, Lingfeng Wan, Thomas W. Crowther, Constantin M. Zohner

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Cultivating crayfish (Procambarus clarkii) significantly enhances the quantity and diversity of soil microorganisms: evidence from the comparison of rice-wheat and rice-crayfish ro... by Hui Xu, Hui Xu, Dan Wang, Dan Wang, Dan Wang, Xuguang Li, Xuguang Li, Jiajia Li, Jiajia Li, Yu Xu, Yu Xu, Zhiqiang Xu, Zhiqiang Xu

“…The Similarity Percentages (SIMPER) analysis indicated that these species also had the highest contribution to the differences in microbial composition between the two groups. Random forest prediction analysis was employed to identify potential biomarkers to distinguish the two microbial communities. …”
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Exploring the impact of aging on motor imagery abilities: a systematic review with meta-analysis by José Fierro-Marrero, José Fierro-Marrero, José Fierro-Marrero, Mario González-Iglesias, Alberto Melis-Romeu, Javier Andrés López-Vidal, Alba Paris-Alemany, Alba Paris-Alemany, Alba Paris-Alemany, Roy La Touche, Roy La Touche, Roy La Touche

“…Mental chronometry in upper limb movements was analyzed visually in forest plot indicating tendencies of greater time in older adults. …”
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RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease by Manuel Lentzen, Srinivasan Vairavan, Marijn Muurling, Vasilis Alepopoulos, Alankar Atreya, Merce Boada, Casper de Boer, Pauline Conde, Jelena Curcic, Giovanni Frisoni, Samantha Galluzzi, Martha Therese Gjestsen, Mara Gkioka, Margarita Grammatikopoulou, Lucrezia Hausner, Chris Hinds, Ioulietta Lazarou, Alexandre de Mendonça, Spiros Nikolopoulos, Dorota Religa, Gaetano Scebba, Pieter Jelle Visser, Gayle Wittenberg, Vaibhav A. Narayan, Neva Coello, Anna-Katharine Brem, Dag Aarsland, Holger Fröhlich, on behalf of RADAR-AD

“…Additionally, we employed four machine learning classifiers – Logistic Regression, Decision Tree, Random Forest, and XGBoost – using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs. …”
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Treatment efficacy for infantile epileptic spasms syndrome in children with trisomy 21 by Henry Chen, Henry Chen, Adam L. Numis, Adam L. Numis, Renée A. Shellhaas, John R. Mytinger, Debopam Samanta, Rani K. Singh, Rani K. Singh, Shaun A. Hussain, Danielle Takacs, Kelly G. Knupp, Li-Rong Shao, Carl E. Stafstrom

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Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin by Marco Bianchini, Mohamed Tarhouni, Matteo Francioni, Marco Fiorentini, Chiara Rivosecchi, Jamila Msadek, Abderrazak Tlili, Farah Chouikhi, Marina Allegrezza, Giulio Tesei, Paola Antonia Deligios, Roberto Orsini, Luigi Ledda, Maria Karatassiou, Athanasios Ragkos, Paride D'Ottavio

“…A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. …”
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Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer by Tiankai Xu, Chu Chu, Shuyu Xue, Tongchao Jiang, Ying Wang, Wen Xia, Huanxin Lin

“…Consequently, we identified four hub genes to formulate a prognostic model, applying Cox regression, LASSO regression, and Random Forest methods. Furthermore, we examined immune infiltration and tumor mutation burden of the genes within our model and scrutinized divergences in the immune microenvironment between high- and low-risk groups. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…We compared 10 ML models based on radiomics features: support vector machine (SVM), logistic regression (LR), random forest, extra trees (ET), naïve Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron (MLP), gradient boosting ML (GBM), light GBM (LGBM), and adaptive boost (AB). …”
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Persistent and emerging threats to Arctic biodiversity and ways to overcome them: a horizon scan by Tanya A. Lemieux, Jackson D.R. Coles, Anne L. Haley, Michelle L. LaFlamme, Sara K. Steel, Kara M. Scott, Jennifer F. Provencher, Courtney Price, Joseph R. Bennett, Isabel C. Barrio, Helen S. Findlay, Simon J. Goodman, Blake Matthews, Joacim Näslund, David. A. Pearce, Robert D. Hollister, Mark L. Mallory, Paul A. Smith, Gabriela Schaepman-Strub, Steven J. Cooke

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Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Develo... by Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai

“…Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. …”
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The association between metabolomic profiles of lifestyle and the latent phase of incident chronic kidney disease in the UK Population by Tingting Jin, Yunqi Wu, Siyi Zhang, Ya Peng, Yao Lin, Saijun Zhou, Hongyan Liu, Pei Yu

“…A total of 249 biological metabolites covering multiple categories were determined by the NMR Metabolomics Platform. Random forest algorithms and LASSO regression were employed to identify lifestyle-related metabolites. …”
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Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference by Yinlei Lei, Min Li, Han Zhang, Yu Deng, Xinyu Dong, Pengyu Chen, Ye Li, Suhua Zhang, Chengtao Li, Shouyu Wang, Ruiyang Tao

“…Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. …”
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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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The Inventory of the Estate farm Senkoniai by Stasys Pamerneckis, Roberta Sakalauskaitė

“…., the quantity of the land and its quality (whether it is a humus, a humus with loam, a sandy loam), its purpose (whether it is arable, used as a hayfield, a pasture, grown with bushes, a forest, or it is barren land) is indicated in it. …”
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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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Eoldist, a Web Application for Estimating Cautionary Detection Distance of Birds by Automatic Detection Systems to Reduce Collisions With Wind Turbines by Julie Fluhr, Olivier Duriez, Constance Blary, Thierry Chambert, Bettina Almasi, Patrik Byholm, Nelleke H. Buitendijk, Jocelyn Champagnon, Mindaugas Dagys, Wolfgang Fiedler, Charlotte Francesiaz, Frédéric Jiguet, Simon Lee, Alexandre Millon, Flavio Monti, Lucile Morcelet, Ran Nathan, Bart A. Nolet, Rascha Nuijten, Philippe Pilard, Cécile Ponchon, Alexandre Roulin, Carlos D. Santos, Orr Spiegel, Kim Schalcher, Aurélie De Seynes, Geert Spanoghe, Martin Wikelski, Ramunas Žydelis, Aurélien Besnard

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Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Subsequently, we retrained and validated our proposed model based on two primary healthcare validation cohorts.Results: In experiments, the algorithms achieved over 88% accuracy on training and test sets. Random Forest (RF) excelled with 96.20% (95% CI 95.39% to 97.02%) accuracy, 96.22% (95% CI 95.40% to 97.03%) specificity, 96.18% (95% CI 95.37% to 97.00%) sensitivity, and 96.20% (95% CI 95.39% to 97.02%) Areas Under Curves (AUC). …”
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Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree by Levionnois, Sébastien, Tysklind, Niklas, Nicolini, Eric, Ferry, Bruno, Troispoux, Valérie, Le Moguedec, Gilles, Morel, Hélène, Stahl, Clément, Coste, Sabrina, Caron, Henri, Heuret, Patrick

“…Apart from soil differences, much variation was found across sites, which calls for further investigation of the factors shaping growth trajectories in tropical forests. …”
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AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism by Lucindah N. Fry-Nartey, Cyril Akafia, Ursula S. Nkonu, Spencer B. Baiden, Ignatus Nunana Dorvi, Kwasi Agyenkwa-Mawuli, Odame Agyapong, Claude Fiifi Hayford, Michael D. Wilson, Whelton A. Miller, Samuel K. Kwofie

“…Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. …”
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Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer by Lin Ni, Lin Ni, He Li, Yanqi Cui, Wanqiu Xiong, Shuming Chen, Hancong Huang, Zhiwei Wang, Hu Zhao, Hu Zhao, Hu Zhao, Bing Wang, Bing Wang, Bing Wang

“…The above 62 DEGs were included in Cox analysis, LASSO regression, Random Forest and SVMV-RFE, respectively, and then the intersection was used to obtain 5 prognostic related characteristic genes (SUV39H2, OPN4, RORB, FBXL6 and SIAH2). …”
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