Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

Aug 4, 2026

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Gut microbiome and high-throughput sequencing

Precision medicine and personalized healthcare

Challenges and the role of machine learning

Scope of the mini-review

TYPE Mini Review PUBLISHED 19 January 2024 DOI 10.3389/fmolb.2023.1337373 OPEN ACCESS EDITED BY Sona Vasudevan, Georgetown University, United States REVIEWED BY Fabio Gervasi, Council for Agricultural and Economics Research (CREA), Italy *CORRESPONDENCE Jingyue Wu, [jingyue.wu@gwu.edu](jingyue.wu@gwu.edu)[ ](jingyue.wu@gwu.edu)Raja Mazumder, [mazumder@gwu.edu](mazumder@gwu.edu) RECEIVED 15 November 2023 ACCEPTED 27 December 2023 PUBLISHED 19 January 2024 CITATION Wu J, Singleton SS, Bhuiyan U, Krammer L and Mazumder R (2024), Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning. Front. Mol. Biosci. 10:1337373 . doi: 10.3389/fmolb.2023.1337373 COPYRIGHT © 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. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning Jingyue Wu 1*, Stephanie S. Singleton 1, Urnisha Bhuiyan 1, Lori Krammer 1,2 and Raja Mazumder 1,3* 1Department 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 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. KEYWORDS precision medicine, machine learning, gut microbiome, metagenomics, multi-omics, sequencing, biomarkers Frontiers in Molecular Biosciences 01 [frontiersin.org](frontiersin.org) Introduction The human body hosts diverse communities of microbes and encompasses various glycomes, fos

FAQs

How does multi-omics analysis contribute to precision medicine using the gut microbiome?

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.

What role do machine learning methods play in studying metagenomics for disease prediction?

Machine learning algorithms identify microbial signatures, predict disease states, and help determine potential therapeutic targets from complex metagenomics datasets.

What challenges remain despite advances in multi-omics, precision medicine, and machine learning?

Key issues include knowledge gaps, selecting appropriate algorithms, and properly parametrizing bioinformatics software for analysis.

Aug 4, 2026

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