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Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning
The 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...
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Published in: | Frontiers in molecular biosciences 2023, Vol.10, p.1337373-1337373 |
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description | The 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. |
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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. 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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.</description><subject>gut microbiome</subject><subject>machine learning</subject><subject>metagenomics</subject><subject>multi-omics</subject><subject>precision medicine</subject><subject>sequencing</subject><issn>2296-889X</issn><issn>2296-889X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpNkctqHDEQRYVJsM3EP-BF0DKbnkiqfqiXwSSOwSGbBLwT1XqMZbqliaQG---j8UxM0EJF1a1bcA8h15xtAeT42S1xnraCCdhygKG-M3IpxNg3Uo4P7_6rL8hVzk-MMd4xGPr2nFyABA6dbC_J8491Lr6Ji9eZ4n6fIupHm2mJNJfVvPiwozvMJUUfis3FB5xpFac4-bhY6gMtj5bqWKfPhUZH98lqn30MdLHGax8sxWDoUn0P9Wwxher6gbx3OGd7dfo35Pe3r79uvjf3P2_vbr7cN7rlUJrJ9a6XtmtBdJMwwuih1xws8KlDJ81kW3fodC3nxvSS4QTc8qlHJjXgABtyd_Q1EZ_UPvkF04uK6NVrI6adwlS8nq1CoftxEFYORramQ2RGStDGaQPjWG9uyKejV43pz1rTUIvP2s4zBhvXrMQoRNsOA0CViqO0JpVzsu7tNGfqAFC9AlQHgOoEsC59PPmvUw3vbeUfLvgL0GWa5A</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Wu, Jingyue</creator><creator>Singleton, Stephanie S</creator><creator>Bhuiyan, Urnisha</creator><creator>Krammer, Lori</creator><creator>Mazumder, Raja</creator><general>Frontiers Media S.A</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>2023</creationdate><title>Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning</title><author>Wu, Jingyue ; Singleton, Stephanie S ; Bhuiyan, Urnisha ; Krammer, Lori ; Mazumder, Raja</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-bf6f68e54325b2d2dc76c13e31b5af8dbe4f76c15411dd680ab31e1b6a08c3a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>gut microbiome</topic><topic>machine learning</topic><topic>metagenomics</topic><topic>multi-omics</topic><topic>precision medicine</topic><topic>sequencing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Jingyue</creatorcontrib><creatorcontrib>Singleton, Stephanie S</creatorcontrib><creatorcontrib>Bhuiyan, Urnisha</creatorcontrib><creatorcontrib>Krammer, Lori</creatorcontrib><creatorcontrib>Mazumder, Raja</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in molecular biosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Jingyue</au><au>Singleton, Stephanie S</au><au>Bhuiyan, Urnisha</au><au>Krammer, Lori</au><au>Mazumder, Raja</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning</atitle><jtitle>Frontiers in molecular biosciences</jtitle><addtitle>Front Mol Biosci</addtitle><date>2023</date><risdate>2023</risdate><volume>10</volume><spage>1337373</spage><epage>1337373</epage><pages>1337373-1337373</pages><issn>2296-889X</issn><eissn>2296-889X</eissn><abstract>The 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. 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subjects | gut microbiome machine learning metagenomics multi-omics precision medicine sequencing |
title | Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning |
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