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random forest algorithm reveals novel sites in HA protein that shift receptor binding preference of the H9N2 avian influenza virus

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Virologica Sinica 2025年第1期40卷 109-117页

作者： Yuncong Yin Wen Li Rujian Chen Xiao Wang Yiting Chen Xinyuan Cui Xingbang Lu David M.Irwin Xuejuan Shen Yongyi Shen College of Veterinary Medicine Yangzhou UniversityYangzhou 225009China Guangdong Laboratory for Lingnan Modern Agriculture State Key Laboratory for Animal Disease Control and PreventionCenter for Emerging and Zoonotic DiseasesCollege of Veterinary MedicineSouth China Agricultural UniversityGuangzhou 510642China Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Disease and Zoonoses Yangzhou UniversityYangzhou 225009China International Joint Laboratory for Cooperation in Agriculture and Agricultural Product Safety Ministry of EducationYangzhou UniversityYangzhou 225009China Department of Laboratory Medicine and Pathobiology University of TorontoToronto M5S1A8Canada Banting and Best Diabetes Centre University of TorontoToronto M5S1A8Canada School of Agriculture and Biology Shanghai Jiao Tong UniversityShanghai 200240China Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control Guangzhou 510642China

A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza *** H9N2 viruses exhibit a preference for binding to human-typeα-2,6 *** identifies their potential threat to public ***,our understanding of the molecular basis for the switch of receptor preference is still *** this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 *** for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding ***,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in *** findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV.

关键词： H9N2 Hemagglutinin(HA) Receptor binding preference random forest algorithm Host shift Interspecies transmission

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random forest algorithm for HR Data Classification and Performance Analysis in Cloud Environments

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 501-508页

作者： Dong, Fangfang Zhengzhou Shengda Univ Econ Business & Management Sch Management Zhengzhou 451191 Henan Peoples R China

This study applies the random forest algorithm to classify and evaluate the effectiveness of business human resources (HR) data, focusing on its potential in supporting strategic decision-making and enhancing organizational efficiency. The research introduces a model that automates the categorization of HR data, including employee records, performance evaluations, and training activities, using the random forest method. By constructing both classification and effectiveness assessment models, the study aims to provide businesses with a robust tool for managing and evaluating employee contributions. Key HR metrics were analyzed and categorized, leading to the creation of an effectiveness evaluation model that offers objective insights into employee performance. The random forest algorithm's accuracy and stability were validated through cross-validation techniques, proving it to be effective in categorizing employee data and identifying different workforce groups. The models developed in this study are designed to support HR managers in optimizing human resource allocation, improving employee satisfaction, and driving overall business performance. The paper also discusses how the model can be optimized further by expanding data sources and applying it to practical business scenarios.

关键词： random forest algorithm business human resources data classification

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Identification of Key lncRNAs in Gout Under Copper Death and Iron Death Mechanisms: A Study Based on ceRNA Network Analysis and random forest algorithm

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MOLECULAR BIOTECHNOLOGY 2025年第3期67卷 996-1013页

作者： Shao, Zi-Chen Sun, Wei-Kang Deng, Qin-Qin Cheng, Ling Huang, Xin Hu, Lie-Kui Li, Hua-Nan Jiangxi Univ Chinese Med Affiliated Hosp 445 Bayi Ave Nanchang 330006 Jiangxi Peoples R China Jiangxi Univ Chinese Med Nanchang 330004 Jiangxi Peoples R China

This study focused on identifying potential key lncRNAs associated with gout under the mechanisms of copper death and iron death through ceRNA network analysis and random forest (RF) algorithm, which aimed to provide new insights into the molecular mechanisms of gout, and potential molecular targets for future therapeutic strategies of gout. Initially, we conducted an in-depth bioinformatics analysis of gout microarray chips to screen the key cuproptosis-related genes (CRGs) and key ferroptosis-related genes (FRGs). Using these data, we constructed a key ceRNA network for gout. Finally, key lncRNAs associated with gout were identified through the RF algorithm combined with ROC curves, and validated using the Comparative Toxicogenomics Database (CTD). We successfully identified NLRP3, LIPT1, and DBT as key CRGs associated with gout, and G6PD, PRKAA1, LIG3, PHF21A, KLF2, PGRMC1, JUN, PANX2, and AR as key FRGs associated with gout. The key ceRNA network identified four downregulated key lncRNAs (SEPSECS-AS1, LINC01054, REV3L-IT1, and ZNF883) along with three downregulated mRNAs (DBT, AR, and PRKAA1) based on the ceRNA theory. According to CTD validation inference scores and biological functions of target mRNAs, we identified a potential gout-associated lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis. This study identified the key lncRNA ZNF883 in the context of copper death and iron death mechanisms related to gout for the first time through the application of ceRNA network analysis and the RF algorithm, thereby filling a research gap in this field and providing new insights into the molecular mechanisms of gout. We further found that lncRNA ZNF883 might function in gout patients by regulating PRKAA1, the mechanism of which was potentially related to uric acid reabsorption in the proximal renal tubules and inflammation regulation. The proposed lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis might represent a potential RNA regulatory pathway for controlling the

关键词： Gout Cuprotosis Ferroptosis lncRNA ceRNA random forest algorithm

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The Intelligent Traffic Safety System Based on 6G Technology and random forest algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Dai, Jianhua Tian, Xiaofan Liu, Lei Zhang, Hao Fu, Jian Yu, Miao China Univ Polit Sci & Law Business Sch Beijing 100000 Peoples R China

The main objective of this study is to explore and evaluate the application potential of combining Sixth Generation (6G) technology and random forest (RF) algorithm in the intelligent traffic safety system. This study designs and implements an intelligent traffic safety system using the RF algorithm and 6G technology, to improve the traffic conditions' real-time monitoring and prediction ability. The advantages of 6G technology in real-time data transmission and efficient data processing. Moreover, the application of the RF algorithm in traffic congestion and accident prediction is discussed. The value of these techniques in improving prediction accuracy, system stability, and safety performance is analyzed. Extensive experimental tests are carried out in multiple traffic scenarios by constructing modules such as data collection and preprocessing, model training and optimization, real-time data processing, and system integration and display. In the experimental test, two main scenarios of traffic congestion warning and accident prediction are designed. The results reveal that in the traffic congestion warning scenario, under the condition that the traffic flow is 1800-2000 vehicles/hour and the average speed is 45-55 km/h, the prediction accuracy reaches 96%, the recall is 99%, and the F1 score is 97%. In the traffic accident prediction scenario, the system's prediction accuracy, recall, and F1 score are 92%, 95%, and 93% when the traffic flow is 1200-1400 vehicles/hour on rainy days. The results of this study provide practical technical solutions for smart city traffic management and explore the prospect of future intelligent transportation system development, thus offering a theoretical and empirical basis for research and practice in related fields.

关键词： Safety Real-time systems 6G mobile communication Radio frequency Accuracy Prediction algorithms Accidents Data communication Data analysis Traffic congestion 6G technology random forest algorithm intelligent transportation traffic safety system real-time monitoring

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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

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Petroleum Science 2024年第2期21卷 1135-1148页

作者： Tie Yan Rui Xu Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng School of Petroleum Engineering Northeast Petroleum UniversityDaqing163318HeilongjiangChina Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya572025HainanChina

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

关键词： Intelligent drilling Closed-loop drilling Lithology identification random forest algorithm Feature extraction

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Particle size distribution measurement in suspension based on a random forest algorithm

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MEASUREMENT & CONTROL 2025年

作者： Yuan, Changquan Shi, Zhanhong Fang, Weihua Huang, Zhenwei Ying, Qifan Xie, Dailiang Liu, Jiufu China Jiliang Univ Key Lab Flow Measurement Technol Zhejiang Prov 258 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China MWR Inspecting & Testing Ctr Hydrol & Geotech Instrume Nanjing Peoples R China Nanjing Inst Water Resources & Hydrol Automation Nanjing Peoples R China

The conversion of ultrasonic particle size distribution (PSD) is traditionally modelled as a first-kind Fredholm integral equation problem, requiring integration with theoretical models to calculate the PSD. In this paper, we propose a novel machine learning-based approach for PSD measurement using an ultrasonic-physical property parameter fusion model coupled with a random forest algorithm. This method employs feature and label parameter training to construct a random forest model that predicts the PSD from input feature parameters, eliminating the need for complex theoretical model selection and inversion algorithms. Forty sets of experimental samples were used to obtain feature parameters for PSD prediction. The results, comparing the predicted PSD of three differently distributed suspended sediment samples with those obtained through a screening method, demonstrate coefficients of determination all above 0.8 and median diameter errors of 6.25%, 2.47% and 1.64%, respectively. In addition, we compared the proposed model with traditional inversion algorithms and the artificial bee colony algorithm, demonstrating that the random forest-based method delivers more accurate and reliable PSD measurements. These findings highlight the potential of this approach as a promising alternative for future studies.

关键词： particle size distribution random forest algorithm ultrasonic attention artificial feature selection

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An improved groundwater vulnerability evaluation model based on random forest algorithm and spatio-temporal change prediction method

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 195卷

作者： Li, Bo Wu, Pan Li, Menghua Chen, Lixia Yang, Lei Long, Jie Guizhou Univ Coll Resources & Environm Engn Key Lab Karst Georesources & Environm Minist Educ Guiyang 550025 Peoples R China China Univ Min & Technol Beijing Coll Earth Sci & Surveying & Mapping Engn Beijing 100083 Peoples R China Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China

Groundwater resource management and protection depend heavily on the assessment of groundwater vulnerability. This study with emphasis on the shallow groundwater in the piedmont alluvial fan region of northern Henan Province, China, aiming to improve the accuracy and applicability of existing groundwater vulnerability evaluation methods. By incorporating additional indicators such as land use types and groundwater quality, the traditional DRASTIC model's indicator system was refined. The random forest algorithm was introduced to determine the weights of these indicators, leading to the establishment of a novel groundwater vulnerability evaluation model. The spatiotemporal patterns of groundwater vulnerability were then predicted by combining this model with a three-dimensional groundwater flow model and examining fluctuations in groundwater level. Evaluation statistics of the research area revealed that the high-risk area of groundwater contamination covered 141.41 km2, 39.78 % of the entire area. Over next 1 and 5 years, the low-risk area of groundwater contamination expanded by 0.34 % and 0.68 %, respectively, due to increased groundwater levels in the southern region. The primary causes of groundwater contamination in the study area were identified as high proportion of plowland and severe pesticide pollution. The shallow groundwater depth, gentle topographic slope, and high permeability coefficient of the vadose zone facilitated the entry of pollutants into the groundwater system. These research findings provide valuable insights for improving groundwater vulnerability evaluation methods.

关键词： Groundwater vulnerability DRASTIC-LQ model random forest algorithm Groundwater flow model Vulnerability prediction

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Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm

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BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Liang, Ying Yin, XingRui Zhang, YangSen Guo, You Wang, YingLong Jiangxi Agr Univ Coll Comp & Informat Engn Zhimin Ave Nanchang Peoples R China Gannan Med Univ Affiliated Hosp 1 Med Coll Rd Ganzhou Peoples R China

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

关键词： LncRNA-protein interactions Multiple features random forest algorithm Features fusion

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Visualizing preferential flow paths using dye tracer and species diversity theory methods to explore their correlation to soil properties with random forest algorithm

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JOURNAL OF HYDROLOGY 2024年 638卷

作者： Zhang, Yinghu Tang, Zhiying Zhang, Jinchi Zhang, Zhenming Zhang, Mingxiang Nanjing Forestry Univ Coinnovat Ctr Sustainable Forestry Southern China Nanjing 210037 Peoples R China Nanjing Forestry Univ Jiangsu Prov Key Lab Soil Eros & Ecol Restorat Nanjing 210037 Peoples R China Nanjing Forestry Univ Prior Acad Program Dev Jiangsu High Educ Inst PAPD Nanjing 210037 Peoples R China Beijing Forestry Univ Sch Ecol & Nat Conservat Beijing 100083 Peoples R China

The preferential flow path development is potentially the result of spatial variations in soil properties with soil depth. However, visualizing the evolution of the preferential flow path with soil depth remains a challenge. This paper presents dye tracer and species diversity theory methods for characterizing preferential flow paths. Field dye tracer experiments were performed at three sites (tree, bush, and grass) in the Yellow River Delta wetland and dye distribution diversity indices (Simpson index (Ds), Shannon-Wiener index (H), Margalef index (Dm), and Pielou index (E)) were applied to verify their availability for preferential flow assessment. The results showed that the uniformity of the shallow-infiltrated dye at the tree site, non-uniformity of the shallow-infiltrated dye at the bush site, and deep dye infiltration at the grass site were the three typical infiltration types. The average proportion of dye-stained areas (PDA) gradually decreased with increasing soil depth. The quantitative effects of soil properties on PDA changes were profound, indicating that soil clay content at 0-10 cm depth, soil sand content at 10-20 cm depth, soil drainage capacity at 20-30 cm depth, and soil bulk density at 30-40 cm depth were the most predictive factors controlling PDA changes. Our results also showed that dye-stained patches with extremely high and high dye concentrations were the most distributed;Ds, H, Dm, and E were the highest at the tree site and E was the diversity index with the greatest importance for PDA change. The findings reveal the soil properties controlling the formation of preferential flow paths, which will improve our understanding of water resource management in the vadose zones of coastal wetlands.

关键词： Soil property Preferential flow path Diversity index Coastal wetland random forest algorithm

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Analyzing the prominence of road accidents on environmental pollution: utilizing machine learning through the random forest algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2024年 1-16页

作者： Al-Mulali, U. Raboshuk, A. Ali, A. Abusham, E. E. A. Ozturk, I. Sohar Univ Fac Business Sohar Oman Sohar Univ Fac Comp & IT Sohar Oman Univ Sharjah Coll Business Adm Sharjah U Arab Emirates Nisantasi Univ Fac Econ Adm & Social Sci Istanbul Turkiye Univ Econ & Human Sci Warsaw Dept Econ Warsaw Poland

Road accidents are believed to play an important social, economic, development and public health role claiming more than a million lives yearly worldwide. Therefore, this research is dedicated to analyzing the effects of road accidents on environmental pollution. To achieve the aim of this research, 41 countries were examined for the 2000-2020 period. Moreover, to harness the power of machine learning, the random forest algorithm is the cornerstone of model implementation in our study. Moreover, for achieving robustness in the outcome of this research five panel models where established utilizing system GMM as the prime method of analysis. Three indicators of road accidents were utilized, namely, deaths, injuries, and accidents that involve casualties. Furthermore, 5 indicators of pollution were examined, i.e., Carbon dioxide, Carbon monoxide, Greenhouse gas, Nitrogen oxides, and Sulphur oxides. The results clearly revealed the positive correlation of road accidents with all environmental pollution indicators investigated. Furthermore, the results analogously revealed that road accidents indicators are predicted to have a significant positive effect on environmental pollution indicators in the future.

关键词： Road accidents Environmental pollution Machine learning random forest algorithm

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