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Current workflows often use single-perspective modeling and lack task-aware prioritization, reducing signal utilization and robustness while blocking traceable transfer from discovery to targeted panels. An interpretable Metabolomics-based Integrated Information Learning (MIIL) framework prioritizes task-relevant metabolites for a compact biomarker panel and improves classification through decision-level fusion of heterogeneous learners.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/metabolomic-analysis-for-diagnosis-of-oralsquamous-cell-carcinoma-using-machine-learning/345543/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/metabolomic-analysis-for-diagnosis-of-oralsquamous-cell-carcinoma-using-machine-learning/345543.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-24","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in OSCC diagnosis?","Question",{"text":112,"@type":113},"The study targets the limitations of existing metabolomics workflows, which often rely on single-perspective modeling and lack task-aware prioritization, leading to underextracted signals and reduced classification robustness.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does MIIL improve OSCC classification?",{"text":117,"@type":113},"MIIL prioritizes task-relevant metabolites to build a compact biomarker panel and strengthens classification by fusing decision-level outputs from heterogeneous learners.",{"name":119,"@type":110,"acceptedAnswer":120},"What diagnostic tasks and performance results are reported?",{"text":121,"@type":113},"Results are reported for CA vs. M1, CA vs. M2, and M1 vs. M2, with mean accuracies of 88.33%, 91.67%, and 85.00%, respectively.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},345543,1790217228,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Yuan et al. Discover Oncology (2026) 17:858 [https://doi.org/10.1007/s12672-026-04988-0](https://doi.org/10.1007/s12672-026-04988-0)  \nDiscover Oncology  \nRESEARCH Open Access  \nMetabolomic analysis for diagnosis of oralsquamous cell carcinoma using machine learning  \nWei Yuan1, Jiayi Rao1, Shang Han1, Sen Li2, Xiangjie Meng2, Lizheng Qin1 and Xin Huang1*  \n*Correspondence:  \nXin Huang  \n[huangxin@ccmu.edu.cn](huangxin@ccmu.edu.cn)[ ](huangxin@ccmu.edu.cn)1Department of Oral and Maxillofacial and Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical University, Beijing 100070, China  \n2School of Biomedical Engineering, Harbin Institute of Technology, Shenzhen, Guangdong  \n518055, China  \nAbstract  \nOral squamous cell carcinoma (OSCC) undergoes significant metabolic reprogramming. Adopting metabolomics to identify altered metabolic enzymes and metabolites holds promise for the early and precise diagnosis of OSCC. However, most current workflows rely on single-perspective modeling and lack task-aware prioritization. They underextract metabolomic signals, limit classification robustness, and impede a traceable transition from untargeted discovery to targeted panels. Therefore, we propose Metabolomics-based Integrated Information Learning (MIIL), an interpretable framework for OSCC diagnosis and premalignant screening. MIIL prioritizes task-relevant metabolites to derive a compact biomarker panel for targeted assay development, while strengthening classification via decision-level fusion of heterogeneous learners. Initially, we collect 120 oral mucosa tissues from 40 OSCC patients, including 40 Cancer (CA) samples, 40 Margin-1 specimens (M1), and 40 Margin-2 tissues (M2) . Moreover, MIIL conducts untargeted metabolomics analysis to identify the most significant differential metabolites contributing to OSCC diagnosis. Subsequently, targeted metabolomics techniques are exploited for in-depth analysis of amino acid metabolites. Finally, integrated learning models are combined via decision-level fusion of linear, probabilistic, and margin-based signals, supporting accurate OSCC classification. The final results demonstrate that this method excelsin the CA.vs. M1, CA.vs. M2, and M1 .vs. M2 diagnostic tasks, achieving mean accuracies of 88. 33%, 91. 67%, and 85. 00%, respectively. Trial registration: Chinese Clinical Trial Registry (ChiCTR), ChiCTR2200064861; registered on 2023-04-23.  \nKeywords Oral squamous cell carcinoma, Metabolomics analysis, Artificial intelligence, Machine learning, Integrated information learning  \n1 Introduction  \nOral squamous cell carcinoma (OSCC) is a common malignancy among head and neck cancers, with a five-year survival rate of approximately 60%. This relatively low survival rate is primarily due to the lack of efficient diagnostic methods and the current inability to identify reliable tumor markers or biological differential indicators [1–3]. Research has shown that the development of OSCC is accompanied by significant metabolic  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of","cbCaipnFa5qLW9DU","https://ap.wps.com/l/cbCaipnFa5qLW9DU","pdf",3290989,24,"English","# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"What problem does the study address in OSCC diagnosis?\",\"answer\":\"The study targets the limitations of existing metabolomics workflows, which often rely on single-perspective modeling and lack task-aware prioritization, leading to underextracted signals and reduced classification robustness.\"},{\"question\":\"How does MIIL improve OSCC classification?\",\"answer\":\"MIIL prioritizes task-relevant metabolites to build a compact biomarker panel and strengthens classification by fusing decision-level outputs from heterogeneous learners.\"},{\"question\":\"What diagnostic tasks and performance results are reported?\",\"answer\":\"Results are reported for CA vs. M1, CA vs. M2, and M1 vs. M2, with mean accuracies of 88.33%, 91.67%, and 85.00%, respectively.\"}]","Metabolomic analysis for diagnosis of oralsquamous cell carcinoma using machine learning | PDF",1790058037]