[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120891-en":3,"doc-seo-120891-105":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},120891,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per-and Polyfluoroalkyl Substances (PFASs)","Per- and polyfluoroalkyl substances (PFASs) present serious health hazards because of their bioactive, persistent, and bioaccumulative behavior, yet evaluating PFAS bioactivity is slow and expensive owing to large-scale in vivo and in vitro experimentation. A semi-supervised learning framework is used to automatically predict PFAS bioactivities across multiple human biological targets, including enzymes, genes, proteins, and cell lines. The method applies semi-supervised metric learning to OECD-listed PFAS species (4730 compounds) and provides a structure–activity relationships analysis for identifying plausible bioactivities across PFAS varieties.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nHarnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Perand Polyfluoroalkyl Substances (PFASs) .  \nPermalink  \n[https://escholarship.org/uc/item/521171r6](https://escholarship.org/uc/item/521171r6)  \nJournal  \nEnvironmental Science & Technology Letters, 10(11)  \nISSN  \n2328-8930  \nAuthors  \nKwon, Hyuna  \nAli, Zulfikhar Wong, Bryan  \nPublication Date  \n2023-11-14  \nDOI  \n10.1021/acs.estlett.2c00530 Peer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nThis article is licensed under CC-BY 4.0   \n[pubs.acs.org/journal/estlcu](pubs.acs.org/journal/estlcu)  Letter   \nHarnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per-and Polyfluoroalkyl Substances (PFASs)  \nHyuna Kwon, Zulfikhar A. Ali, and Bryan M. Wong*  \n Cite This: Environ. Sci. Technol. Lett. 2023, 10, 1017−1022  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nABSTRACT: Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both timeconsuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semisupervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Cooperation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure−activity relationships for predicting possible bioactivities in a variety of PFAS species.  \nKEYWORDS: per- and polyfluoroalkyl substances, PFAS, machine learning, bioactivity, semi-supervised learning  \n■ INTRODUCTION  \nSince the 1930s, 1 per-and polyfluoroalkyl substances (PFASs) have been used in several consumer products (including firefighting foams) due to their outstanding stability and water/oil repellant properties.2 However, these compounds pose significant risks to the environment and biosystems. The presence of PFASs in surface water and groundwater can result in exposure to organisms, subsequently leading to accumulation in the body, with adverse effects on the liver, kidneys, blood, and immune system.2,3 Because of these deleterious effects, there is a pressing need to identify and understand the bioactivity of PFAS-based compounds that can adversely affect human health.  \nFor these reasons, several international groups including the Organisation of Economic Co-operation and Development (OECD), United States Environmental Protection Agency, Food and Drug Administration, European Chemicals Agency, European Food Safety Authority, and Ministry of Ecology and Environment (China) continue to monitor PFASs that are produced in the global market.4,5 According to a 2018 OECD report, more than 4700 PFASs currently exist as manufacturers bring new forms of PFASs into industrial and consumer products (it is worth pointing out, however, that not all 4700 structures exist in commerce). Nevertheless, among the wide varieties of PFAS molecules, the potential hazards of these new forms remain largely unknown.  \nDue to the sheer number of PFAS species, in vivo and in vitro biological experiments are both time-consuming and costly. As such, the construction of predictive and reliable quantitative-  \nstructure activity relationship (QSAR) models6−8 is essential for assessing the bioactivities of these contaminants (even for PFAS species that are","cbCaifmtA0s8KAJ0","https://ap.wps.com/l/cbCaifmtA0s8KAJ0","pdf",2642907,1,7,"English","en",105,"# Abstract\n# Introduction\n## Background on PFAS hazards and exposure\n## Need for predictive QSAR models\n## Limitations of prior supervised machine-learning approaches\n## Proposed semi-supervised metric learning framework","[{\"question\":\"Why is bioactivity assessment for PFASs difficult?\",\"answer\":\"PFAS bioactivity evaluation requires extensive in vivo and in vitro experiments, making it time-consuming and costly due to the large number and variety of PFAS compounds.\"},{\"question\":\"What machine learning approach is used to predict PFAS bioactivities?\",\"answer\":\"Semi-supervised metric learning models are used to learn structure–bioactivity relationships while leveraging mostly unlabeled data with limited labeled examples.\"},{\"question\":\"Which PFAS dataset and targets does the study focus on?\",\"answer\":\"Predictions are made for PFASs from a recent OECD report list containing 4730 compounds, with bioactivities predicted across targets such as enzymes, genes, proteins, and cell lines.\"}]","Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per-and Polyfluoroalkyl Substances (PFASs) | 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is bioactivity assessment for PFASs difficult?","Question",{"text":75,"@type":76},"PFAS bioactivity evaluation requires extensive in vivo and in vitro experiments, making it time-consuming and costly due to the large number and variety of PFAS compounds.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approach is used to predict PFAS bioactivities?",{"text":80,"@type":76},"Semi-supervised metric learning models are used to learn structure–bioactivity relationships while leveraging mostly unlabeled data with limited labeled examples.",{"name":82,"@type":73,"acceptedAnswer":83},"Which PFAS dataset and targets does the study focus on?",{"text":84,"@type":76},"Predictions are made for PFASs from a recent OECD report list containing 4730 compounds, with bioactivities predicted across targets such as enzymes, genes, proteins, and cell 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