Modeling habitat suitability for endangered herb (Salvia leriifolia Benth) using Innovative hybrid machine learning algorithms by Emran Dastres, Hamidreza Rabiei-Dastjerdi, Hassan Esmaeili, Mahdis Amiri, Ali Sonboli, Mohammad Hossein Mirjalili

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A pilot randomized aerobic exercise trial in older HIV-infected men: Insights into strategies for successful aging with HIV. by Krisann K Oursler, John D Sorkin, Alice S Ryan, Leslie I Katzel

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Microseismic Data-Driven Short-Term Rockburst Evaluation in Underground Engineering with Strategic Data Augmentation and Extremely Randomized Forest by Shouye Cheng, Xin Yin, Feng Gao, Yucong Pan

“…The insights derived from this research provide a reference for microseismic data-based short-term rockburst prediction when faced with class imbalance and multicollinearity.…”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

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Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy by Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin

“…Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. …”
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The abundance and sett ecological characteristics of the Asian badger (Meles leucurus) in the Northeast Tiger and Leopard National Park (NE China) by Jiawei Li, Dongrui Li, Yanwen Fu, Yiwen Ma, Jianping Ge, Limin Feng

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Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning anal... by Abhijeet Das

“…This study sought to evaluate the region's surface water quality and sources of contamination using machine learning (ML) methods such as Logistic Regression (LOR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN). …”
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Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms by Yicheng Wang, Yicheng Wang, Yicheng Wang, Chuan-Yang Wu, Hui-Xian Fu, Jian-Cheng Zhang, Jian-Cheng Zhang, Jian-Cheng Zhang

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Chinese Massage (Tuina) Attenuates Knee Osteoarthritis by Modulating Autophagy-Related Cytokines: A Multidisciplinary Methodological Investigation by Wang Z, Zhao C, Li M, Zhang L, Diao J, Wu Y, Yang T, Shi M, Lei Y, Wang Y, Li M, Bian Y, Zhou Y, Xu H

“…Zhen Wang,1,* Chi Zhao,1,2,* Mengmeng Li,1,2,* Lili Zhang,1,* Jieyao Diao,1 Yiming Wu,2 Tao Yang,2 Mingwei Shi,2 Yang Lei,2 Yu Wang,3 Miaoxiu Li,4 Yanqin Bian,5 Yunfeng Zhou,1 Hui Xu1,2 1College of Acupuncture and Massage, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 2Acupuncture and Massage Department, The Third Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 3College of Computer Science, Xidian University, Xian, People’s Republic of China; 4College of Acupuncture and Massage, Shanghai University of Chinese Medicine, Shanghai, People’s Republic of China; 5Orthopaedic Research Laboratory, University of California, Davis, CA, USA*These authors contributed equally to this workCorrespondence: Hui Xu, Email 15036065036@163.comBackground: Tuina therapy has demonstrated its potential in modulating autophagy-related factors in knee osteoarthritis (KOA); however, its core therapeutic targets and specific mechanisms require systematic elucidation through interdisciplinary research.Objective: This study investigated the mechanism by which Tuina alleviates KOA progression using multidimensional approaches, including Mendelian randomization (MR), in vivo experiments, and machine learning.Methods: Genetic data from genome-wide association studies of 60 cytokines and KOA were analyzed using MR analysis to identify autophagy-related factors significantly associated with KOA. …”
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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

“…To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. …”
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The protocol for a multicentre prospective randomized noninferiority trial of surgical reduction versus non-surgical casting for displaced distal radius fractures in children: Chil... by Daniel C. Perry, Juul Achten, James Mason, Daphne Kounail, Nicolas Nicolaou, David Metcalfe, Mark Lyttle, Elizabeth Tutton, Duncan Appelbe, Phoebe Gibson, Matthew L. Costa

“…Conclusion: At six weeks, and three, six, and 12 months, data on function, pain, QoL, cosmesis, and satisfaction with care will be collected. …”
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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques by Manjog Padhy, Umar Muhammad Modibbo, Rasmita Rautray, Subhranshu Sekhar Tripathy, Sujit Bebortta

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Enhanced Viral Genome Classification Using Large Language Models by Hemalatha Gunasekaran, Nesaian Reginal Wilfred Blessing, Umar Sathic, Mohammad Shahid Husain

“…Among these are traditional algorithms such as Random Forest (RF), K-nearest neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB), each offering unique advantages in handling genetic data. …”
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph by Chris Jennings-Shaffer, David H. Rich, Matthew Macaulay, Michael D. Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A. Suchard, Frederick A. Matsen

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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

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Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households by Seok Min Ji, Jeewuan Kim, Kyu Min Kim

“…The classification model was developed using four machine learning algorithms: Random Forest, Gradient boosting, Decision tree, Ridge regression, Neural network, and AdaBoost. …”
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Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating Hig... by Xie Y, Yang M, Huang J, Jiang Z

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Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Pro... by Zixiang Ye, Zhangyu Lin, Enmin Xie, Chenxi Song, Rui Zhang, Hao-Yu Wang, Shanshan Shi, Lei Feng, Kefei Dou

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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Stud... by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

“…Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. …”
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