Microsatellite instability and somatic gene variant profile in solid organ tumors by Ibrahim Halil Erdogdu, Seda Orenay-Boyacioglu, Olcay Boyacioglu, Nesibe Kahraman-Cetin, Habibe Guler, Merve Turan, Ibrahim Meteoglu

Get full text

Identification of EGR1 as a Key Diagnostic Biomarker in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Through Machine Learning and Immune Analysis by Wu X, Pan T, Fang Z, Hui T, Yu X, Liu C, Guo Z, Liu C

“…We employed three machine learning methods—LASSO, SVM, and Random Forest (RF)—to identify hub genes associated with MASLD. …”
Get full text

Improved cattle farm classification: leveraging machine learning and linked national datasets by Guy-Alain Schnidrig, Guy-Alain Schnidrig, Rahel Struchen, Sara Schärrer, Dagmar Heim, Daniela Hadorn, Gertraud Schüpbach-Regula, Giulia Paternoster

“…Among these models, the Random Forest model demonstrated the highest level of performance, achieving an accuracy of 0.914 (95% CI: 0.890, 0.938) and an F1-Score of 0.879 (95% CI: 0.841, 0.913). …”
Get full text

Rabies re-emergence after long-term disease freedom (Amur Oblast, Russia) by A. D. Botvinkin, I. D. Zarva, I. V. Meltsоv, S. A. Chupin, E. M. Poleshchuk, N. G. Zinyakov, S. V. Samokhvalov, I. V. Solovey, N. V. Yakovleva, G. N. Sidorov, I. A. Boyko, V. G. Yudin, E. I. Andaev, A. Ye. Metlin

“…After 2018, the epizootic spread within the forest-steppe landscapes of the Zeya-Bureya Plain, where human and animal rabies cases had been earlier reported (until 1972). …”
Get full text

Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Results Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. …”
Get full text

Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy by Yingchun Ren, Xiaoying Shan, Gengchao Ding, Ling Ai, Weiying Zhu, Ying Ding, Fuzhou Yu, Yun Chen, Beijiao Wu

“…Thirteen machine learning techniques, including Random Forest, Support Vector Machine, and Artificial Neural Network, were employed. …”
Get full text

Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques by Yue-Shan Chang, Shu-Ting Huang, Basanta Haobijam, Satheesh Abimannan, Takayuki Kushida

“…In this study, we evaluate the proposed model's performance using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2), alongside comparative analyses against SVR (Support Vector Regression), AdaBoost, and RF (Random Forest) models. The results show that STH-MLR-LSTM achieves the best average prediction results across the six locations. …”
Get full text

Assessing national exposure to and impact of glacial lake outburst floods considering uncertainty under data sparsity by H. Chen, Q. Liang, J. Zhao, S. B. Maharjan

“…In the innovative framework, multi-temporal imagery is utilised with a random forest model to extract glacial lake water surfaces. …”
Get full text

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. …”
Get full text

Retrieval of Land Surface Temperature From Passive Microwave Observations Using CatBoost-Based Adaptive Feature Selection by Yang Dai, Yingbao Yang, Xin Pan, Penghua Hu, Xiangjin Meng, Fanggang Li, Zhenwei Wang

“…We compared the accuracy of the proposed method with the Holmes, multichannel, and Random Forest algorithms. Results showed that the proposed method had lowest RMSE, with the value of 3.28 K (1.95 K), 2.69 K (1.65 K), and 3.71 K (2.22 K) on grassland, cropland, and barren land at daytime (nighttime), respectively. …”
Get full text

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. …”
Get full text

Sunlight exposure practice and its associated factors among infants in Ethiopia, systematic review and meta-analysis. by Fanos Yeshanew Ayele, Natnael Kebede, Asnakew Molla Mekonen, Mengistu Mera Mihiretu, Yawkal Tsega, Elsabeth Addisu, Niguss Cherie, Tesfaye Birhane, Chala Daba, Ahmed Hussien Asfaw, Zinet Abegaz, Abel Endawekie, Anissa Mohammed, Dagnachew Melak, Fekade Demeke Bayou, Husniya Yasin, Aregash Abebayehu Zerga, Fikre Bayu Gebeyehu, Birhanu Wagaye

“…Meta-analysis was conducted by using STATA 17 software. Forest plots were used to present the pooled prevalence of good sunlight exposure practices. …”
Get full text

Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence by Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza, Francesca Ardito, Anna Maria Stellacci, Afwa Thameur, Sergio Ruggieri, Sabina Tangaro, Marcello Mastrorilli, Nicola Sanitate, Simone Pietro Garofalo

“…The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). …”
Get full text

Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province by Rongwei Li, Pengwei Wang, Shucheng Tan, Yangbiao Zhou, Lifeng Liu, Chaodong Gou, Yalan Yu

“…This correction framework corrects the susceptibility results of the Random Forest (RF) model, which is based on 12 static factors and historical hazard data, using surface deformation data measured by the SBAS-InSAR technique. …”
Get full text

A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes by Jingzhen Li, Xiaojing Ma, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, Jingyi Lu, Wei Lu, Yuqian Bao, Jian Zhou, Zedong Nie

“…In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. …”
Get full text

Effect of K-N-Humates on Dry Matter Production and Nutrient Use Efficiency of Maize in Sarawak, Malaysia by Auldry Chaddy Petrus, Osumanu Haruna Ahmed, Ab Majid Nik Muhamad, Hassan Mohammad Nasir, Make Jiwan

Get full text

Individual mobility prediction by considering current traveling features and historical activity chain by Xiaotong Zhang, Zhipeng Gui, Yuhang Liu, Dehua Peng, Qianxi Lan, Zhangxiao Shen, Huan Chen, Yuhui Zuo, Yao Yao, Huayi Wu, Kai Li, Kun Qin

“…It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. …”
Get full text

Securing black lion tamarin populations: improving habitat-based inputs and risks for population viability analysis to inform management decisions by Francy Forero-Sánchez, Francy Forero-Sánchez, Gabriela Cabral Rezende, Gabriela Cabral Rezende, Cláudio Valladares-Pádua, Cláudio Valladares-Pádua, Fabio Stucchi Vannucchi, Fabio Stucchi Vannucchi, Leandro Jerusalinsky, Leandro Jerusalinsky, Luciana Pacca, Kathy Traylor-Holzer

“…The Endangered black lion tamarin (Leontopithecus chrysopygus) survives in 17 fragments of the Atlantic Forest within the Paranapanema River basin, in southeast Brazil, with an estimated 2,255 individuals. …”
Get full text

Exploring the Prevalence and Components of Metabolic Syndrome in Sub-Saharan African Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis by Nelson Musilanga, Hussein Nasib, Given Jackson, Frank Shayo, Clarkson Nhanga, Saleh Girukwigomba, Ambokile Mwakibolwa, Samson Henry, Keneth Kijusya, Edgar Msonge

“…The summary estimates were presented with forest plots and tables. Publication bias was checked with the funnel plot and Egger’s regression test. …”
Get full text

The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. Most of the athletes originated from the USA (1786), followed by athletes from Germany (674), Canada (426), Australia (396), United Kingdom (342), France (325), and Switzerland (276). …”
Get full text