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Genome-wide methylation data from cancerous and normal tissues were used, and methods were tested across three urological cancer types. A decision tree selected diagnostic methylation sites, and a neural network then classified samples as cancerous or non-cancerous.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/diagnostic-classification-based-on-dna-methylation-profiles-using-sequential-machine-learning-approaches-research-article/328798/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/diagnostic-classification-based-on-dna-methylation-profiles-using-sequential-machine-learning-approaches-research-article/328798.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-01","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the study address?","Question",{"text":63,"@type":64},"It targets how to identify DNA methylation biomarkers that can support diagnostic classification of cancer patients.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were diagnostic methylation sites selected?",{"text":68,"@type":64},"A decision tree was used to identify the methylation sites most useful for diagnosis.",{"name":70,"@type":61,"acceptedAnswer":71},"How were samples classified as cancerous or non-cancerous?",{"text":72,"@type":64},"The selected methylation locations were used to train a neural network that classified samples into cancerous versus non-cancerous groups.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},328798,1790143755,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":145,"read_time":146,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","PLOS ONE  \nOPEN ACCESS  \nCitation: Wojewodzic MW, Lavender JP (2024) Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches. PLoS ONE 19(9): e0307912 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0307912](doi.org/10.1371/journal.pone.0307912)  \n[Editor:](Editor: Marc Reismann)[ Marc Reismann](Editor: Marc Reismann), Charit´e  \nUniversita¨tsmedizin Berlin CVK: Charite  \nUniversitatsmedizin Berlin-Campus VirchowKlinikum, GERMANY  \nReceived: December 4, 2023  \nAccepted: July 10, 2024  \nPublished: September 6, 2024  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0307912](https://doi.org/10.1371/journal.pone.0307912)  \n[Copyright:](Copyright:) © [2024 Wojewodzic](2024 Wojewodzic), Lavender. This isan open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: * The source code is available in GitHub repository: [https://github.com/](https://github.com/)[ ](https://github.com/)bazyliszek/methAI and in Zenodo: [https://doi.org/](https://doi.org/)  \nRESEARCH ARTICLE  \nDiagnostic classification based on DNA methylation profiles using sequential machine learning approaches  \nMarcin W. Wojewodzic1,2,3 *, Jan P. Lavender3  \n1 Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway, 2 Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway, 3 University of Birmingham, Birmingham, United Kingdom  \n* [Marcin.Wojewodzic@kreftregisteret.no](Marcin.Wojewodzic@kreftregisteret.no)  \nAbstract  \nAberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.  \nIntroduction  \nEpigenetics is the study of biochemical modifications, heritable through cell division, carrying information independent of DNA sequence [ 1] . This information has direct consequences for cellular phenotype and cell differentiation, including cancer formation. Although the biochemical modifications can vary for DNA, methylation signatures seem to be most optimal in terms of tradeoffs between variation and stability to provide data which can be used for disease diagnosis. This, together with recent breakthroughs and the decreasing cost of sequencing technology, is a probable reason that methylation is the branch of epigenetics that has progressed furthest in the last decade.  \nMore importantly, clear evidence has emerged in recent years that aberrant DNA methylation plays a key role in many diseases, including cancer [2–4] . As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer [2] . For","cbCaiehuSRKFfKgB","https://ap.wps.com/l/cbCaiehuSRKFfKgB","pdf",1400561,13,"English","# Abstract\n# Introduction","[{\"question\":\"What problem does the study address?\",\"answer\":\"It targets how to identify DNA methylation biomarkers that can support diagnostic classification of cancer patients.\"},{\"question\":\"How were diagnostic methylation sites selected?\",\"answer\":\"A decision tree was used to identify the methylation sites most useful for diagnosis.\"},{\"question\":\"How were samples classified as cancerous or non-cancerous?\",\"answer\":\"The selected methylation locations were used to train a neural network that classified samples into cancerous versus non-cancerous groups.\"}]","Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches - research article | PDF",1789981038,33]