[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123087-en":3,"doc-seo-123087-105":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},123087,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma - evaluating single biomarkers","Integrating omics data with machine learning for early oral squamous cell carcinoma (OSCC) detection is investigated through precision metabolomics. A multicenter public dataset with 61 OSCC patients and 61 healthy controls supports plasma metabolomics feature extraction and model development. Extra Trees selects informative variables, while TabPFN performs classification and prediction. Using top-ranked individual biomarkers yields strong diagnostic performance (AUC 93%) and clinically relevant metabolic signatures that distinguish OSCC from healthy controls for potential non-invasive screening and personalized intervention.","TYPE Original Research PUBLISHED 03 December 2024 DOI 10.3389/fimmu.2024.1493377  \nOPEN ACCESS  \nEDITED BY  \nWenyi Jin,  \nCity University of Hong Kong, Hong Kong SAR, China  \nREVIEWED BY  \nCheng Wang,  \nSun Yat-sen University, China Lin-Lin Bu,  \nWuhan University, China Chunjie Li,  \nSichuan University, China  \n*CORRESPONDENCE  \nJiannan Liu  \n [liujiannan@sh9hospital.org.cn](liujiannan@sh9hospital.org.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 09 September 2024  \nACCEPTED 18 November 2024  \nPUBLISHED 03 December 2024  \nCITATION  \nSun Y, Cheng G, Wei D, Luo J and Liu J (2024) Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.  \nFront. Immunol. 15:1493377 .  \ndoi: 10.3389/fimmu.2024.1493377  \nCOPYRIGHT  \n© 2024 Sun, Cheng, Wei, Luo and Liu. This isan 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.  \nIntegrating omics data and machine learning techniques for precision detection of oralsquamous cell carcinoma:  \nevaluating single biomarkers  \nYilan Sun 1,2,3,4,5,6†, Guozhen Cheng 7†, Dongliang Wei 1,2,3,4,5,6, Jiacheng Luo 2 and Jiannan Liu 1,2,3,4,5,6*  \n1 Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2College of Stomatology, Shanghai Jiao Tong University, Shanghai, China, 3 National Center for Stomatology, Shanghai, China, 4 National Clinical Research Center for Oral Diseases, Shanghai, China, 5Shanghai Key Laboratory of Stomatology, Shanghai, China, 6Shanghai Research Institute of Stomatology, Shanghai, China, 7College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, China  \nIntroduction: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes . Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived biomarkers.  \nMethods: We analyzed a multicenter public dataset comprising 61 OSCC patients and 61 healthy controls. Plasma metabolomics data were processed to extract 29 numerical and 47 ratio features. The Extra Trees (ET) algorithm was applied for feature selection, and the TabPFN model was used for classification and prediction.  \nResults: The model achieved an area under the curve (AUC) of 93% and an overall accuracy of 76 . 6% when using top-ranked individual biomarkers. Key metabolic features significantly differentiated OSCC patients from healthy controls, providing a detailed metabolic fingerprint of the disease.  \nDiscussion: Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.  \nKEYWORDS  \nmachine learning, oral squamous cell carcinoma, precision metabolomics, featureselection, personalized therapy  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nOral squamous cell carcinoma (OSCC) is the most common malignancy affecting the oral cavity, with a mortality rate exceeding 50%(1). Postoperative OSCC can severely impact patients’ speech, chewing, and swallowing functions, significantly affecting their quality of life (2). Many OSCC patients are diagnosed at advanced stages, missing the window for optimal treatment. Early diagnosis is c","cbCaip037Gfwe4G5","https://ap.wps.com/l/cbCaip037Gfwe4G5","pdf",5659962,1,14,"English","en",105,"# Introduction\n## Rationale for early OSCC detection\n## Limitations of imaging and biopsies\n## Rationale for plasma metabolomics\n# Methods\n## Dataset and study cohort\n## Feature extraction from plasma metabolomics\n## Feature selection and classification models\n# Results\n## Diagnostic performance metrics\n## Distinctive metabolic biomarkers\n# Discussion\n## Implications for non-invasive diagnostic tools\n## Actionable metabolic signatures","[{\"question\":\"What dataset and study design are used for the OSCC analysis?\",\"answer\":\"The study uses a multicenter public dataset containing 61 OSCC patients and 61 healthy controls, with plasma metabolomics data used to build diagnostic models.\"},{\"question\":\"Which machine learning methods are applied for feature selection and classification?\",\"answer\":\"Extra Trees is used for feature selection, and the TabPFN model is used for classification and prediction.\"},{\"question\":\"How well does the model perform using top-ranked individual biomarkers?\",\"answer\":\"When using top-ranked individual biomarkers, the model reaches an AUC of 93% and an overall accuracy of 76.6%.\"}]","Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma - evaluating single biomarkers | PDF",1785814584,35,{"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},"integrating-omics-data-and-machine-learning-techniques-for-precision-detection-of-oral-squamous-cell-carcinoma-evaluating-single-biomarkers","",{"@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/integrating-omics-data-and-machine-learning-techniques-for-precision-detection-of-oral-squamous-cell-carcinoma-evaluating-single-biomarkers/123087/",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","2026-08-04",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 dataset and study design are used for the OSCC analysis?","Question",{"text":75,"@type":76},"The study uses a multicenter public dataset containing 61 OSCC patients and 61 healthy controls, with plasma metabolomics data used to build diagnostic models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are applied for feature selection and classification?",{"text":80,"@type":76},"Extra Trees is used for feature selection, and the TabPFN model is used for classification and prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How well does the model perform using top-ranked individual biomarkers?",{"text":84,"@type":76},"When using top-ranked individual biomarkers, the model reaches an AUC of 93% and an overall accuracy of 76.6%.","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"]