[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126874-en":3,"doc-seo-126874-105":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},126874,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches","Aberrant methylation patterns in human DNA offer strong potential for discovering diagnostic and disease-progression biomarkers. This study applies machine learning to identify informative methylation sites and to classify patients from methylation values at those sites. Genome-wide methylation data from cancerous and normal tissues are used, and methods are tested on three urological cancer types. A decision tree selects key sites, and a neural network performs two-step cancer vs non-cancer classification, yielding biomarker panels for each cancer.","University of Birmingham  \nDiagnostic classification based on DNA methylation profiles using sequential machine learning approaches  \nWojewodzic, Marcin W.; Lavender, Jan P.  \nDOI:  \n10.1371/journal.pone.0307912  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nWojewodzic, MW & Lavender, JP 2024, 'Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches', PLOS One, vol. 19, no. 9, e0307912 .  \n[https://doi.org/10.1371/journal.pone.0307912](https://doi.org/10.1371/journal.pone.0307912)  \nLink to publication on Research at Birmingham portal  \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  \nPLOS 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 Hea","cbCaiqh9HBWFgAoi","https://ap.wps.com/l/cbCaiqh9HBWFgAoi","pdf",1441274,1,14,"English","en",105,"# Abstract\n# Introduction\n# Methods overview\n## Feature selection with decision tree\n## Classification with neural network","[{\"question\":\"What biological signal does the study use for diagnosis?\",\"answer\":\"The study uses aberrant DNA methylation patterns as the biomarker signal, leveraging methylation values across genome-wide sites.\"},{\"question\":\"How are methylation sites selected before classification?\",\"answer\":\"A decision tree identifies the methylation sites most useful for distinguishing cancerous from non-cancerous samples.\"},{\"question\":\"How are patients classified after site selection?\",\"answer\":\"The selected methylation locations train a neural network that classifies samples as either cancerous or non-cancerous.\"}]","Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches | 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