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Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models

  • Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

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Metadaten
URN:urn:nbn:de:kobv:526-opus4-19889
Publisher DOI:https://doi.org/10.3389/fmicb.2024.1516667
Author:Piotr PrzymusORCiD, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak TruuORCiD, Enrique Carrillo De Santa Pau, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia SampriORCiD, Marcus FrohmeORCiDGND, Alina NechyporenkoORCiD
ISSN:1664-302X
Parent Title (English):Frontiers in Microbiology
Document Type:Article
Language:English
Year of Publication:2025
Volume:15
Article Number:1516667
Publisher:Frontiers
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Publishing Institution:Technische Hochschule Wildau
Tag:clasiffication; clustering; deep learning; microbiome; natural language processing
Source:Przymus P, Rykaczewski K, Martín-Segura A, Truu J, Carrillo De Santa Pau E, Kolev M, Naskinova I, Gruca A, Sampri A, Frohme M and Nechyporenko A (2025) Deep learning in microbiome analysis: a comprehensive review of neural network models. Front. Microbiol. 15:1516667. doi: 10.3389/fmicb.2024.1516667
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 579 Mikroorganismen, Pilze, Algen
Funding:Publikationsfonds der TH Wildau
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2025/01/29
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