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This narrative review synthesizes 2020–2026 evidence on multi-omics machine learning methods, covering integration strategies, algorithms, and clinical uses. Deep learning and multi-omics fusion outperform single-omics models, improving survival subtype prediction and risk stratification through architectures such as ensemble frameworks, graph neural networks, transformers, and attention mechanisms. Key challenges include harmonizing data, improving interpretability, and validating clinically.",{"@graph":69,"@context":123},[70,84,106],{"@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/integrative-multi-omics-machine-learning-for-precision-cancer-prognostication/290882/",{"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/integrative-multi-omics-machine-learning-for-precision-cancer-prognostication/290882.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",23,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why are traditional cancer prognostic models considered limited?","Question",{"text":113,"@type":114},"They rely mainly on clinicopathological features and do not capture the molecular heterogeneity and biological complexity driving tumor behavior, leading to constrained predictive accuracy.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How do multi-omics machine learning approaches improve cancer prognostication?",{"text":118,"@type":114},"They integrate complementary biological layers (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics), enabling models to discover biomarkers and relationships that single-omics approaches miss.",{"name":120,"@type":111,"acceptedAnswer":121},"Which modeling techniques are highlighted as effective for capturing complex relationships?",{"text":122,"@type":114},"Graph neural networks, transformer-based architectures, and attention mechanisms are emphasized for modeling inter-omics interactions more effectively.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290882,1789642878,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"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":130,"read_time":144,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","International Journal of Multidisciplinary Research in Biotechnology, Pharmacy,  \nDental and Medical Sciences (IJMRBPDMS)  \nIntegrative Multi-Omics Machine Learning for Precision Cancer  \nPrognostication  \nDr. Vikram J. Patel¹, Dr. Ananya T. George2,  \n1MD, DM Senior Consultant and Academic Lead, Oncology Research, Comprehensive Cancer Sciences Centre  \nMumbai, India, [vikram.patel@ccsc.org.in](vikram.patel@ccsc.org.in)  \n2MD, Professor of Immuno-Oncology, Centre for Cancer Immunotherapy Research  \nKochi, India, [ananya.george@ccir.edu.in](ananya.george@ccir.edu.in)  \nABSTRACT  \nBackground:  \nBackground: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decisionmaking and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate limited predictive accuracy due to the inherent molecular heterogeneity of malignancies.This narrative review examines recent advances in multi-omics machine learning approaches for cancer prognosis prediction, synthesizing evidence from 2020–2026 on integration strategies, algorithmic methodologies, and clinical applications across major cancer types.Deep learning enables the analysis of highdimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. Multi-omics integration incorporating genomics, transcriptomics, epigenomics, proteomics, and metabolomics consistently outperforms single-omics approaches. DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches, robustly predicts patient survival subtypes using multi-omics data and yields significantly better risk-stratification than other multi-omics integration methods. Graph neural networks, transformer-based architectures, and attention mechanisms have emerged as powerful tools for capturing complex inter-omics relationships. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. These models demonstrate substantial improvements in survival prediction, recurrence risk stratification, and treatment response assessment across breast, lung, colorectal, liver, and hematological malignancies. Meta-learning, spatial multi-omics, and federated learning are pivotal directions for realizing the clinical translation of next-generation precision oncology. Addressing challenges in data harmonization, model interpretability, and prospective validation remains essential for clinical implementation.  \nKeywords: Multi-omics, machine learning, deep learning, cancer prognosis, precision oncology, data integration, survival prediction  \nArticle Info  \nReceived: 05 February 2026, received in revised form: 15 February 2026, accepted: 20 February 2026, available online:  \n27 February 2026; Volume: 2; Issue: 2; Pages: 01-10.  \nCitation: Ram J. K., 2026. Integrative Multi-Omics Machine Learning for Precision Cancer Prognostication. International Journal of  \nMultidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences (IJMRBPDMS) 2(2): 01-10.  \nDOI:  \nIntroduction  \nCancer represents one of the most significant global health challenges, accounting for millions of deaths annually and imposing substantial burdens on healthcare systems worldwide. Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, pronounced molecular heterogeneity, and variable therapeutic responses. (5) The clinical management of cancer patients critically depends on accurate prognostic assessment to guide treatment decisions, optimize therapeutic strategies, and improve survival outcomes. Traditional prognostic models rely primarily on clinicopathological features including tumor stage, histological grade, and performance status. However, these conventional approaches demonstrate signif","cbCaieWHsnnwtZi0","https://ap.wps.com/l/cbCaieWHsnnwtZi0","pdf",411526,"English","# Abstract\n# Introduction\n# Recent Advances in Multi-Omics and Machine Learning","[{\"question\":\"Why are traditional cancer prognostic models considered limited?\",\"answer\":\"They rely mainly on clinicopathological features and do not capture the molecular heterogeneity and biological complexity driving tumor behavior, leading to constrained predictive accuracy.\"},{\"question\":\"How do multi-omics machine learning approaches improve cancer prognostication?\",\"answer\":\"They integrate complementary biological layers (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics), enabling models to discover biomarkers and relationships that single-omics approaches miss.\"},{\"question\":\"Which modeling techniques are highlighted as effective for capturing complex relationships?\",\"answer\":\"Graph neural networks, transformer-based architectures, and attention mechanisms are emphasized for modeling inter-omics interactions more effectively.\"}]","Integrative Multi-Omics Machine Learning for Precision Cancer Prognostication | PDF",25]