[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120082-en":3,"doc-seo-120082-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120082,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis","Lupus nephritis is a challenging autoimmune condition with limited diagnostic and treatment options. This study applies 12 distinct machine learning algorithms together with non-negative matrix factorization to kidney single-cell datasets from biopsy samples, aiming to build immune-related gene predictive frameworks. The approach identifies immune cell populations and constructs 102 predictive models, with top-performing models achieving high AUC and external-cohort validation. Six hub IRGs serve as noninvasive diagnostic markers, and clinical correlations and PPI networks support key roles in LN pathophysiology.","TYPE Original Research PUBLISHED 19 August 2024  \nDOI 10.3389/fimmu.2024.1391218  \nOPEN ACCESS  \nEDITED BY  \nCarlo Perricone, University of Perugia, Italy  \nREVIEWED BY  \nRoberto Dal Pozzolo, University of Perugia, Italy Simone Parisi,  \nUniversity Hospital of the City of Health and Science of Turin, Italy  \n*CORRESPONDENCE Xiaoyan Huang  \n [huangxiaoyan@pku.org.cn](huangxiaoyan@pku.org.cn)[ ](huangxiaoyan@pku.org.cn)Meiying Wang  \n[wmy99wmy99@163.com](wmy99wmy99@163.com)[ ](wmy99wmy99@163.com)Zuhui Pu  \n [zuhuipu@email.szu.edu.cn](zuhuipu@email.szu.edu.cn)  \nRECEIVED 25 February 2024  \nACCEPTED 23 July 2024  \nPUBLISHED 19 August 2024  \nCITATION  \nMou L, Lu Y, Wu Z, Pu Z, Huang X and Wang M (2024) Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis.  \nFront. Immunol. 15:1391218 .  \ndoi: 10.3389/fimmu.2024.1391218  \nCOPYRIGHT  \n© 2024 Mou, Lu, Wu, Pu, Huang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nApplying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis  \nLisha Mou 1,2, Ying Lu 1,2, Zijing Wu 1,2, Zuhui Pu 3*, Xiaoyan Huang 4* and Meiying Wang 1*  \n1 Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China, 2 MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China, 3 Imaging Department, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital,  \nShenzhen, China, 4 Department of Nephrology, Peking University Shenzhen Hospital, Shenzhen, China  \nLupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.  \nKEYWORDS  \nsystemic lupus erythematosus, lupus nephritis, scRNA-seq, immune-related genes, NMF, machine learning, prediction model, PPI  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \n1 In","cbCaisMC6dzOmdnD","https://ap.wps.com/l/cbCaisMC6dzOmdnD","pdf",5116088,1,17,"English","en",105,"# Introduction\n## Role of renal-infiltrating immune cells in lupus nephritis\n## Value of single-cell RNA sequencing (scRNA-seq)\n## Machine learning in biomedical predictive modeling","[{\"question\":\"What methods are used to build robust prediction models for lupus nephritis?\",\"answer\":\"The study applies 12 distinct machine learning algorithms combined with non-negative matrix factorization (NMF) to analyze kidney single-cell datasets from biopsy samples.\"},{\"question\":\"How many predictive models are constructed, and how are they evaluated?\",\"answer\":\"The analysis constructs 102 immune-related gene predictive models. The best models show high predictive accuracy using AUC and are validated in external cohorts.\"},{\"question\":\"Which hub immune-related genes are highlighted as key diagnostic markers?\",\"answer\":\"Six hub IRGs—CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR—are identified as key diagnostic markers with strong diagnostic performance in renal and peripheral blood cohorts.\"}]","Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis | PDF",1785728052,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"applying-12-machine-learning-algorithms-and-non-negative-matrix-factorization-for-robust-prediction-of-lupus-nephritis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/applying-12-machine-learning-algorithms-and-non-negative-matrix-factorization-for-robust-prediction-of-lupus-nephritis/120082/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What methods are used to build robust prediction models for lupus nephritis?","Question",{"text":75,"@type":76},"The study applies 12 distinct machine learning algorithms combined with non-negative matrix factorization (NMF) to analyze kidney single-cell datasets from biopsy samples.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many predictive models are constructed, and how are they evaluated?",{"text":80,"@type":76},"The analysis constructs 102 immune-related gene predictive models. The best models show high predictive accuracy using AUC and are validated in external cohorts.",{"name":82,"@type":73,"acceptedAnswer":83},"Which hub immune-related genes are highlighted as key diagnostic markers?",{"text":84,"@type":76},"Six hub IRGs—CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR—are identified as key diagnostic markers with strong diagnostic performance in renal and peripheral blood cohorts.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]