[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123757-en":3,"doc-seo-123757-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123757,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning","The gastrointestinal (gut) microbiome is a key determinant of host health and is increasingly leveraged for precision medicine. High-throughput sequencing has enabled high-resolution generation of -omics datasets, including metagenomics, metatranscriptomics, glycomics, and metabolomics, supporting personalized therapy discovery. Multi-omics analyses reveal functional genes, microbial composition, glycans, and metabolites, enabling biomarker identification for diagnosis, prognosis, and treatment. Machine learning further extracts signatures and predicts disease states, while challenges persist in knowledge gaps, algorithm choice, and bioinformatics parameterization.","TYPE Mini Review  \nPUBLISHED 19 January 2024  \nDOI 10.3389/fmolb.2023.1337373  \nOPEN ACCESS  \nEDITED BY  \nSona Vasudevan,  \nGeorgetown University, United States  \nREVIEWED BY  \nFabio Gervasi,  \nCouncil for Agricultural and Economics Research (CREA), Italy  \n*CORRESPONDENCE  \nJingyue Wu,  \n [jingyue.wu@gwu.edu](jingyue.wu@gwu.edu)[ ](jingyue.wu@gwu.edu)Raja Mazumder,  \n [mazumder@gwu.edu](mazumder@gwu.edu)  \nRECEIVED 15 November 2023  \nACCEPTED 27 December 2023  \nPUBLISHED 19 January 2024  \nCITATION  \nWu J, Singleton SS, Bhuiyan U, Krammer L and Mazumder R (2024), Multi-omics approaches to  \nstudying gastrointestinal microbiome in the context of precision medicine and machine learning.  \nFront. Mol. Biosci. 10:1337373 .  \ndoi: 10.3389/fmolb.2023.1337373  \nCOPYRIGHT  \n© 2024 Wu, Singleton, Bhuiyan, Krammer and Mazumder. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMulti-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning  \nJingyue Wu 1*, Stephanie S. Singleton 1, Urnisha Bhuiyan 1, Lori Krammer 1,2 and Raja Mazumder 1,3*  \n1Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States, 2Milken Institute School of Public Health, The George Washington University, Washington, DC, United States, 3The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC, United States  \nThe human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various-omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other-omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.  \nKEYWORDS  \nprecision medicine, machine learning, gut microbiome, metagenomics, multi-omics, sequencing, biomarkers  \nFrontiers in Molecular Biosciences 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nThe human body hosts diverse communities of microbes and encompasses various glycomes, fos","cbCaipprRtDhkgJL","https://ap.wps.com/l/cbCaipprRtDhkgJL","pdf",877386,1,"English","en",105,"# Introduction\n## Gut microbiome and high-throughput sequencing\n## Precision medicine and personalized healthcare\n## Challenges and the role of machine learning\n## Scope of the mini-review","[{\"question\":\"How does multi-omics analysis contribute to precision medicine using the gut microbiome?\",\"answer\":\"Multi-omics integrates information such as microbial composition, functional genes, glycans, and metabolites, enabling personalized therapeutic insights and biomarker discovery for diagnosis, prognosis, and treatment.\"},{\"question\":\"What role do machine learning methods play in studying metagenomics for disease prediction?\",\"answer\":\"Machine learning algorithms identify microbial signatures, predict disease states, and help determine potential therapeutic targets from complex metagenomics datasets.\"},{\"question\":\"What challenges remain despite advances in multi-omics, precision medicine, and machine learning?\",\"answer\":\"Key issues include knowledge gaps, selecting appropriate algorithms, and properly parametrizing bioinformatics software for analysis.\"}]","Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning | 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does multi-omics analysis contribute to precision medicine using the gut microbiome?","Question",{"text":74,"@type":75},"Multi-omics integrates information such as microbial composition, functional genes, glycans, and metabolites, enabling personalized therapeutic insights and biomarker discovery for diagnosis, prognosis, and treatment.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What role do machine learning methods play in studying metagenomics for disease prediction?",{"text":79,"@type":75},"Machine learning algorithms identify microbial signatures, predict disease states, and help determine potential therapeutic targets from complex metagenomics datasets.",{"name":81,"@type":72,"acceptedAnswer":82},"What challenges remain despite advances in multi-omics, precision medicine, and machine learning?",{"text":83,"@type":75},"Key issues include knowledge gaps, selecting appropriate algorithms, and properly parametrizing bioinformatics software for 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