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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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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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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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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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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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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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Object-based classification of hyperspectral data using random forest algorithm

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Geo-Spatial Information Science 2018年第2期21卷 127-138页

作者： Saeid Amini Saeid Homayouni Abdolreza Safari Ali A.Darvishsefat School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran Department of Geography Environment and GeomaticsUniversity of OttawaOttawaCanada Faculty of Natural Resources Department of ForestryUniversity of TehranKarajIran

This paper presents a new framework for object-based classification of high-resolution hyperspectral *** multi-step framework is based on multi-resolution segmentation(MRS)and random forest classifier(RFC)*** first step is to determine of weights of the input features while using the object-based approach with MRS to processing such *** the high number of input features,an automatic method is needed for estimation of this ***,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image ***,based on this parameter and other required parameters,the image is segmented into some homogenous ***,the RFC is carried out based on the characteristics of segments for converting them into meaningful *** proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial *** data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral *** experimental results show that the proposed method is more consistent for land cover mapping in various *** overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,***,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.

关键词： Object-based classification random forest algorithm multi-resolution segmentation(MRS) hyperspectral imagery

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Microbiome analysis and random forest algorithm-aided identification of the diacetyl-producing microorganisms in the stacking fermentation stage of Maotai-flavor liquor production

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FOOD BIOTECHNOLOGY 2019年第4期33卷 338-352页

作者： Wang, Li Liu, Yanfeng Yang, Fan Li, Jianghua Chen, Bi Du, Guocheng China Kweichow Moutai Distillery Co Ltd Zunyi Guizhou Peoples R China Jiangnan Univ Key Lab Carbohydrate Chem & Biotechnol Minist Educ Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Key Lab Ind Biotechnol Minist Educ Wuxi Jiangsu Peoples R China

Diacetyl imparts a characteristic buttery aroma to foods and beverages. However, when present at high levels in Maotai-flavor liquor, it generates an undesirable buttery off-odor. The aim of this study was to identify the diacetyl-producing microorganisms in Maotai-flavor liquor with an ultimate aim to develop methods that could reduce diacetyl production. To explore the microbial origin of diacetyl in Maotai-flavor liquor, the off-odor intensity and diacetyl content of stacking fermented grains, which are used in the production of Maotai-flavor liquor, were evaluated. Then, 16S rRNA V3-V4 high-throughput sequencing and the random forest algorithm were combined to analyze the potential diacetyl producers. The featured selection results indicated that Lactobacillus was the major diacetyl contributor in the fermented grains, followed by Staphylococcus, Weissella, Pediococcus, and Klebsiella. To verify this, 123 lactic acid bacteria (LAB) isolates were selected from the high diacetyl off-odor group samples, and the genus Lactobacillus accounted for more than 90% of the identified isolates. The results of our study showed that L. plantarum, L. pentosus, and L. fermentum were the major diacetyl-producing microorganisms (>90 mg/L). Moreover, the fermentation characteristics of the high-yield strain, L. plantarum MTL-09, showed that temperature and pH had strong effects on diacetyl production, which may offer a strategy for inhibiting the off-odor by controlling the pH or temperature during fermentation. The methods may also be useful for identification of key microorganisms for fermented foods and alcoholic beverages.

关键词： Lactic acid bacteria high-throughput sequencing diacetyl random forest algorithm Maotai-flavor liquor of China

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Application of the random forest algorithm to predict the corrosion losses of carbon steel over the first year of exposure in various regions of the world

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CORROSION ENGINEERING SCIENCE AND TECHNOLOGY 2023年第3期58卷 205-213页

作者： Gavryushina, Marina Marshakov, Andrey Panchenko, Yuliya Russian Acad Sci IPCE RAS AN Frumkin Inst Phys Chem & Electrochem Moscow Russia

The random forest (RF) algorithm was used to develop two models for predicting the first-year corrosion losses (C-1) of carbon steel in open air in various regions of the world. The first RF model built using combined databases of international programmes ISO CORRAG, MICAT and ECE/UN and tests conducted in Russia is intended for estimation of C-1 in various types of atmospheres in various regions of the world. The second RF model enables the prediction of C-1 in continental areas of the world. The accuracy of C-1 predictions by the two RF and two dose-response functions, i.e. the function presented in ISO 9223 standard and the new version for a non-marine atmosphere, was compared. The reliability of the two RF models was shown to be significantly higher than that of the dose-response functions with exception of the predictions for corrosion losses of carbon steel in regions of Russia with a cold climate.

关键词： atmospheric corrosion steel machine learning random forest algorithm dose-response function

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Investigation of Nuclear Binding Energy and Charge Radius Based on random forest algorithm

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原子能科学技术 2023年第4期57卷 704-712页

作者： CAI Boshuai YU Tianjun LIN Xuan ZHANG Jilong WANG Zhixuan YUAN Cenxi Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China

The random forest algorithm was applied to study the nuclear binding energy and charge *** regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random *** for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,*** interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of *** significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding *** the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present *** significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible *** effect is verified by the shell-corrected nuclear charge radius model.

关键词： nuclear binding energy nuclear charge radius random forest algorithm

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