[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124945-en":3,"doc-seo-124945-105":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},124945,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Application of Machine Learning in Nanotoxicology - Critical Review and Perspective - Abstract","The massive production and application of nanomaterials (NMs) have raised concerns about their potential adverse effects on human health and the environment. Evaluating these hazards requires integrating complex experimental and exposure data. This critical review examines how machine learning can support nanotoxicology by improving prediction, prioritization, and risk assessment, while discussing key methodological challenges, data quality issues, and practical implementation perspectives.","University of Birmingham  \nApplication of Machine Learning in Nanotoxicology  \nZhou, Yunchi; Wang, Ying; Peijnenburg, Willie; Vijver, Martina G. ; Balraadjsing, Surendra; Dong, Zhaomin; Zhao, Xiaoli; Leung, Kenneth M.Y.; Mortensen, Holly M.; Wang, Zhenyu;  \nLynch, Iseult; Afantitis, Antreas; Mu, Yunsong; Wu, Fengchang; Fan, Wenhong DOI:  \n10.1021/acs.est.4c03328  \nLicense:  \nOther (please specify with Rights Statement)  \nDocument Version  \nPeer reviewed version  \nCitation for published version (Harvard):  \nZhou, Y, Wang, Y, Peijnenburg, W, Vijver, MG, Balraadjsing, S, Dong, Z, Zhao, X, Leung, KMY, Mortensen, HM, Wang, Z, Lynch, I, Afantitis, A, Mu, Y, Wu, F & Fan, W 2024, 'Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective', Environmental Science and Technology, vol. 58, no. 34, pp.  \n14973–14993. [https://doi.org/10.1021/acs.est.4c03328](https://doi.org/10.1021/acs.est.4c03328)  \nLink to publication on Research at Birmingham portal  \nPublisher Rights Statement:  \nThis document is the Accepted Manuscript version of a Published Work that appeared in final form in Environmental Science and Technology, copyright © 2024 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [https://doi.org/10.1021/acs.est.4c03328](https://doi.org/10.1021/acs.est.4c03328)  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 04. Aug. 2026  \n1 Application of Machine Learning in Nanotoxicology: a  \n2 Critical Review and Perspective  \n3  \n4 Yunchi Zhou a,b, Ying Wang a*, Willie Peijnenburg c,d, Martina G. Vijver c,  \n5 Surendra Balraadjsing c, Zhaomin Dong a,e, Xiaoli Zhao f, Kenneth M. Y. Leung g,  \n6 Holly M. Mortensen h, Zhenyu Wang i, Iseult Lynch j, Antreas Afantitis k, Yunsong Mu l,  \n7 Fengchang Wu f, Wenhong Fan a,e* 8  \n9 Author affiliations:  \n10 a School of Materials Science and Engineering, Beihang University, Beijing 100191, China  \n11 b Ecole Centrale de Pékin/School of General Engineering, Beihang University, Beijing  \n12 100191. China  \n13 c Institute of Environmental Science, Leiden University, Leiden, The Netherlands  \n14 d National Institute of Public Health and the Environment, Center for Safety of Products  \n15 and Substances, Bilthoven, The Netherlands  \n16 e Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang  \n17 University, Beijing 100191, China  \n18 f State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research  \n19 Academy of Environmental Sciences, Beijing 100012, China  \n20 ","cbCaibWES8GQYoQ7","https://ap.wps.com/l/cbCaibWES8GQYoQ7","pdf",1727627,1,68,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Scope and objectives\n# Machine learning methods in nanotoxicology\n## Data sources and preprocessing\n## Modeling and prediction\n# Evaluation and challenges\n## Validation and uncertainty\n## Interpretability and generalization\n# Perspective and future directions","[{\"question\":\"Why does nanotoxicology increasingly rely on machine learning?\",\"answer\":\"The widespread production and use of nanomaterials create concerns about potential adverse health and environmental effects, making hazard evaluation more complex and data-intensive. Machine learning is used to improve prediction and support prioritization in risk assessment.\"},{\"question\":\"What does the review cover regarding applying ML to nanotoxicology?\",\"answer\":\"It focuses on how machine learning can help evaluate adverse effects, emphasizing prediction and prioritization through the integration of experimental and exposure information.\"},{\"question\":\"What key concerns does the review highlight about applying ML?\",\"answer\":\"The review addresses methodological limitations, including issues related to data quality and the broader challenges involved in validating and deploying ML approaches for nanotoxicology.\"}]","Application of Machine Learning in Nanotoxicology - Critical Review and Perspective - Abstract | PDF",1785895533,171,{"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},"application-of-machine-learning-in-nanotoxicology-critical-review-and-perspective-abstract","",{"@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/application-of-machine-learning-in-nanotoxicology-critical-review-and-perspective-abstract/124945/",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","2026-08-05",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},"Why does nanotoxicology increasingly rely on machine learning?","Question",{"text":75,"@type":76},"The widespread production and use of nanomaterials create concerns about potential adverse health and environmental effects, making hazard evaluation more complex and data-intensive. Machine learning is used to improve prediction and support prioritization in risk assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the review cover regarding applying ML to nanotoxicology?",{"text":80,"@type":76},"It focuses on how machine learning can help evaluate adverse effects, emphasizing prediction and prioritization through the integration of experimental and exposure information.",{"name":82,"@type":73,"acceptedAnswer":83},"What key concerns does the review highlight about applying ML?",{"text":84,"@type":76},"The review addresses methodological limitations, including issues related to data quality and the broader challenges involved in validating and deploying ML approaches for nanotoxicology.","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"]