[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120073-en":3,"doc-seo-120073-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120073,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Harmonizing Multi-Omics for Enhanced Machine Learning","High-throughput omics technologies generate large-scale data across genomics, epigenomics, transcriptomics, proteomics, and metabolomics layers. Machine learning models can use this information to support diagnostic and classification biomarker discovery, yet many biomarkers still depend on single-omics measurements and miss biological complexity captured by multi-omics experiments. Effective multi-omics integration strategies are therefore essential. This minireview organizes recent integration methods into early, mixed, intermediate, late, and hierarchical strategies, emphasizing challenges and machine-learning applications.","Harmonizing Multi-Omics for Enhanced  \nMachine Learning  \nPraveen Kumar N K, Nayan Murthy  \nJain University, Bengalore, Karnataka, India  \n[1](1p.kumarnk@gmail.com)[p.kumarnk@gmail.com](1p.kumarnk@gmail.com)  \nAbstract - The proliferation of high-throughput technologies has yielded an abundance of omics data, spanning diverse biological layers such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Machine learning algorithms have harnessed this data deluge, yielding diagnostic and classification biomarkers. However, prevailing biomarkers predominantly rely on single omic measurements, overlooking the potential insights from multi-omics experiments that encapsulate the entirety of biological complexity. To fully exploit the wealth of information embedded in different omics layers, effective multi-omics data integration strategies become imperative. This minireview categorizes recent integration  \nmethods/frameworks into five strategies: early, mixed, intermediate, late, and hierarchical. Our focus is on delineating challenges and exploring existing multi-omics integration strategies, with a keen emphasis on their application in machine learning.  \nKeywords - High-throughput technologies, Omics data, Machine learning algorithms, Multi-omics experiments, Data integration strategies  \nI. INTRODUCTION (SIZE 10 &BOLD) The emergence of cost-effective and potent screening technologies [1] has ushered in a new era of extensive biological data, paving the way for advancements in therapeutics and personalized medicine [2] . Variances in treatment effectiveness and adverse effects among individuals, attributable to factors like age, sex, genetics, and environmental influences (e.g., anthropometric and metabolic status, dietary habits, lifestyle [3,4]), underscore the importance of precision medicine. The objective is to tailor interventions based on individual biological information [5]  \nClinical and omics data can be sourced directly from databases or gathered through screening technologies for applications such as disease analysis [6], class prediction [7], biomarker discovery [8], disease subtyping, enhanced system biology understanding [9], and drug repurposing. Each omics data type represents a distinct \"layer\" of biological information, such as genomics, epigenomics, transcriptomics, proteomics, and  \nmetabolomics, offering complementary perspectives on biological systems or individuals. Historical single-omics studies aimed to uncover the causes of pathologies and guide appropriate treatments, but current understanding acknowledges the complexity of diseases involving intricate molecular pathways with interactions across different biological layers.  \nTo navigate existing approaches, a classification system is essential for selecting suitable methods and identifying best practices. Zitnik et al. (2019) [10] categorized integration into horizontal and vertical types. This mini-review focuses on vertical integration, where each omics dataset shares the same rows (samples) but different variables (omics features) . We assume that the datasets are already processed, normalized, or scaled based on their omics type. Existing general reviews on vertical integration [11] often categorize methods by mathematical aspects, such as Bayesian, network-based, deep learning-based, kernel-based, or matrix factorization-based methods.  \nII. Contributions  \nMultiple goals, including sample classification, disease subtyping, and biomarker discovery, can be achieved with multiple omics datasets. However, integrating these datasets, each with the same rows (representing samples) and different columns (representing biological variables), poses challenges. Machine learning (ML) models are commonly employed, but integrating multiple noisy and high-dimensional datasets requires careful consideration. Various integration strategies have been developed, each with its pros and cons. Assuming proper pre-processing of each dataset, a simp","cbCaipSMFYEUHVIP","https://ap.wps.com/l/cbCaipSMFYEUHVIP","pdf",109282,1,4,"English","en",105,"# Introduction\n## Contributions\n## Related Work\n## Dimensionality Reduction","[{\"question\":\"Why is multi-omics data integration important for machine learning?\",\"answer\":\"Single-omics biomarkers often overlook biological complexity. Multi-omics integration enables models to exploit complementary information across different omics layers.\"},{\"question\":\"How does the minireview categorize multi-omics integration strategies?\",\"answer\":\"It categorizes recent methods into five strategies: early, mixed, intermediate, late, and hierarchical integration.\"},{\"question\":\"What role does dimensionality reduction play in multi-omics analysis?\",\"answer\":\"Dimensionality reduction helps decrease noise and simplify high-dimensional omics datasets, improving computing efficiency and model performance. Feature selection and feature extraction are key approaches.\"}]","Harmonizing Multi-Omics for Enhanced Machine Learning | PDF",1785727996,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"harmonizing-multi-omics-for-enhanced-machine-learning","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/harmonizing-multi-omics-for-enhanced-machine-learning/120073/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is multi-omics data integration important for machine learning?","Question",{"text":74,"@type":75},"Single-omics biomarkers often overlook biological complexity. Multi-omics integration enables models to exploit complementary information across different omics layers.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the minireview categorize multi-omics integration strategies?",{"text":79,"@type":75},"It categorizes recent methods into five strategies: early, mixed, intermediate, late, and hierarchical integration.",{"name":81,"@type":72,"acceptedAnswer":82},"What role does dimensionality reduction play in multi-omics analysis?",{"text":83,"@type":75},"Dimensionality reduction helps decrease noise and simplify high-dimensional omics datasets, improving computing efficiency and model performance. Feature selection and feature extraction are key approaches.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]