[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121336-en":3,"doc-seo-121336-105":30,"detail-sidebar-cat-0-en-105":83},{"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},121336,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota","The gut microbiome is central to human health and disease, making its detailed characterization valuable for diagnostic and therapeutic development. Multi-omics approaches—metagenomics, metatranscriptomics, metabolomics, and metaproteomics—capture the ecosystem’s complexity but create large, integrative data streams. Conventional statistics may struggle to derive clinically actionable signals. Machine learning and artificial intelligence are increasingly applied to multi-omics datasets across microbiome-disruption contexts, enabling biomarker discovery, treatment response prediction, and refinement of microbiome-modulating therapies, while evaluating current capabilities and limitations.","1 2  \n3 4  \n5 6  \n7 8  \n9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n50  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \n60  \nGastroenterology 2025; -:1–15  \nQ1 Machine Learning and Artificial Intelligence in the Multi-Omics Q2 Approach to Gut Microbiota  \nQ18 Tommaso Rozera1 ,2 ,3 Edoardo Pasolli4 Nicola Segata5 ,6 Gianluca Ianiro1 ,2 ,3  \n1Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy; 2Department of Medical and Surgical Sciences, L’Unità Operativa Complessa Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy; 3Department of Medical and Surgical Sciences, L’Unità Operativa Complessa Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy; 4University of Naples Federico II, Department of Agricultural Sciences, Piazza Carlo di Borbone 1, Portici, Italy; 5Department CIBIO, University of Trento, Trento, Italy; and 6Department of Experimental Oncology, European Institute of Oncology Istituto di Ricovero e Cura a Carattere  \nQ3 Q4 Scientifico, Milan, Italy  \nThe gut microbiome is involved in human health and disease, and its comprehensive understanding is necessary to exploit it as a diagnostic or therapeutic tool. Multi-omics approaches, including metagenomics, metatranscriptomics, metabolomics, and metaproteomics, enable depiction of the gut microbial ecosystem’s complexity. However, these tools generate a large data stream in which integration is needed to produce clinically useful readouts, but, in turn, might be difficult to carry out with conventional statistical methods. Artificial intelligence and machine learning have been increasingly applied to multiomics datasets in several conditions associated with microbiome disruption, from chronic disorders to cancer. Such tools have potential for clinical implementation, including discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiomemodulating therapies. The state of the art, potential, and limits, of artificial intelligence and machine learning in the multi-omics approach to gut microbiome are discussed.  \nKeywords: Machine Learning; Artificial Intelligence; Gut Microbiome; Precision Medicine.  \nThe human gut microbiome refers to the ecosystem  \nformed by trillions of micro-organisms, spanning bacteria, viruses, fungi, and archaea, that inhabit the gastrointestinal tract. These microbes are critical to human health because they influence immune maturation, regulation of metabolic pathways, processing of nutrients, drug metabolism, and several other functions.1,2 The microbiome is a dynamic entity and it changes depending on several factors, such as the type of delivery, diet, drugs, and other  \nenvironmental factors.2 Moreover, the gut itself consists of several distinct local ecosystems that make microbial communities change significantly across different regions of the gastrointestinal tract.3,4 The imbalance of the microbiome homeostasis has been linked to a number of chronic diseases, including metabolic disorders,5 systemic autoimmune conditions,6 and cancer.7 Increasing evidence highlights that the gut microbiome plays a pivotal role in modulating immune responses, influencing carcinogenesis, and shaping therapeutic responses.6, 7 A comprehensive understanding of gut microbiome composition and functions and diseaserelated shifts is necessary to exploit the microbiome as a diagnostic and/or a therapeutic tool in clinical medicine. To unravel the dynamics that regulate the gut microbiome in health and disease, integrative approaches able to retrieve not only data on mic","cbCaihrgUsYaVQO4","https://ap.wps.com/l/cbCaihrgUsYaVQO4","pdf",894497,1,15,"English","en",105,"# Overview\n## Multi-omics and gut microbiome complexity\n## Why integration is challenging\n## Role of machine learning and AI\n# Applications and clinical potential\n## Biomarker discovery\n## Treatment response prediction\n## Microbiome-modulating therapy refinement\n# Current state, opportunities, and limits","[{\"question\":\"How are AI and machine learning used with multi-omics gut microbiome data?\",\"answer\":\"They are applied to datasets across conditions associated with microbiome disruption to support discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiome-modulating therapies.\"}]","Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota | PDF",1785735129,38,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-and-artificial-intelligence-in-the-multi-omics-approach-to-gut-microbiota","",{"@graph":36,"@context":77},[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/machine-learning-and-artificial-intelligence-in-the-multi-omics-approach-to-gut-microbiota/121336/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How are AI and machine learning used with multi-omics gut microbiome data?","Question",{"text":75,"@type":76},"They are applied to datasets across conditions associated with microbiome disruption to support discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiome-modulating therapies.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]