[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125954-en":3,"doc-seo-125954-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125954,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","miRNA in Machine-Learning-Based Diagnostics of Oral Cancer - Abstract","MicroRNAs (miRNAs) regulate gene expression and are implicated in cancer pathogenesis through abnormal miRNA expression patterns. Conventional oral cancer diagnosis via biopsy and histopathology is invasive, costly, and requires specialized interpretation, motivating the need for accessible, non-invasive diagnostic alternatives. This study builds machine-learning models using miRNA sequence features, target gene associations, and cancer-specific signaling pathways, and evaluates classifiers including BayesNet, validated across cross-validation and independent datasets.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nmiRNA in Machine-Learning-Based Diagnostics of Oral Cancer.  \nPermalink  \n[https://escholarship.org/uc/item/7d85s5vk](https://escholarship.org/uc/item/7d85s5vk)  \nJournal  \nBiomedicines, 12(10)  \nISSN  \n2227-9059  \nAuthors  \nLi, Xinghang Kouznetsova, Valentina Tsigelny, Igor  \nPublication Date  \n2024-10-21  \nDOI  \n10.3390/biomedicines12102404  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n biomedicines  \nArticle  \nmiRNA in Machine-Learning-Based Diagnostics of Oral Cancer  \nXinghang Li 1, Valentina L. Kouznetsova 1,2 and Igor F. Tsigelny 1,2,3, *  \nCitation: Li, X.; Kouznetsova, V.L.; Tsigelny, I.F. miRNA in MachineLearning-Based Diagnostics of Oral Cancer. Biomedicines 2024, 12, 2404 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)biomedicines12102404  \nAcademic Editor: Marie ˇCerná  \nReceived: 22 August 2024  \nRevised: 23 September 2024  \nAccepted: 12 October 2024  \nPublished: 21 October 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 IUL Scientific Program, La Jolla, CA 92038, USA; vkouznetsova@ucsd.edu (V.L.K.)  \n2 San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA  \n3 Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA  \n* Correspondence: [itsigeln@ucsd.edu](itsigeln@ucsd.edu)  \nAbstract: Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives. Objective: This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways. Results: The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model’s effectiveness was further validated using independent datasets, confirming its potential clinical utility. Discussion: Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives. Conclusions: This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.  \nKeywords: miRNA; machine learning; oral cancer; diagnostics  \n1. Introduction  \nMicroRNAs (miRNAs) are small, noncoding RNAs critical in regulating gene expression at the post-transcriptional level, influencing key cellular processes such as development, differentiation, apoptosis, and proliferation [1] . Their role extends into the realm of disease pathogenesis, particularly in cancer, where the dysregulation of miRNA expression has been identified as a significant factor [2] . Traditional approaches to cancer diagnosis, including biopsies and histopathological analyses, are invasive, costly, and demand specialized interpretive skills [3] . These methods are becom","cbCaibir4PqOb7X2","https://ap.wps.com/l/cbCaibir4PqOb7X2","pdf",1571524,10,1,13,"English","en",105,"# Abstract\n# Introduction\n## Motivation and need for non-invasive diagnostics\n## Role of miRNA and AI in diagnostics","[{\"question\":\"Why are microRNAs (miRNAs) relevant to oral cancer diagnosis?\",\"answer\":\"miRNAs regulate gene expression and their dysregulation is linked to cancer pathogenesis. Abnormal miRNA expression patterns can serve as diagnostic signals for oral cancer.\"},{\"question\":\"How do the machine-learning models use miRNA information in this research?\",\"answer\":\"The study generates miRNA attributes including sequence characteristics, target gene associations, and cancer-specific signaling pathway information. These features feed multiple ML algorithms for classification.\"},{\"question\":\"What performance did the best classifier achieve?\",\"answer\":\"The BayesNet classifier performed best, reaching 95% accuracy and an AUC of 0.98 during cross-validation, and its effectiveness was further supported using independent datasets.\"}]","miRNA in Machine-Learning-Based Diagnostics of Oral Cancer - Abstract | PDF",1785902211,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"mirna-in-machine-learning-based-diagnostics-of-oral-cancer-abstract","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/mirna-in-machine-learning-based-diagnostics-of-oral-cancer-abstract/125954/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are microRNAs (miRNAs) relevant to oral cancer diagnosis?","Question",{"text":77,"@type":78},"miRNAs regulate gene expression and their dysregulation is linked to cancer pathogenesis. Abnormal miRNA expression patterns can serve as diagnostic signals for oral cancer.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How do the machine-learning models use miRNA information in this research?",{"text":82,"@type":78},"The study generates miRNA attributes including sequence characteristics, target gene associations, and cancer-specific signaling pathway information. 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