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,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1516667/full |
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| author | Piotr Przymus Krzysztof Rykaczewski Adrián Martín-Segura Jaak Truu Enrique Carrillo De Santa Pau Mikhail Kolev Mikhail Kolev Irina Naskinova Aleksandra Gruca Alexia Sampri Alexia Sampri Marcus Frohme Alina Nechyporenko Alina Nechyporenko |
| author_facet | Piotr Przymus Krzysztof Rykaczewski Adrián Martín-Segura Jaak Truu Enrique Carrillo De Santa Pau Mikhail Kolev Mikhail Kolev Irina Naskinova Aleksandra Gruca Alexia Sampri Alexia Sampri Marcus Frohme Alina Nechyporenko Alina Nechyporenko |
| author_sort | Piotr Przymus |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6e1293d24cff4b989017e4965add671d |
| institution | Kabale University |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj-art-6e1293d24cff4b989017e4965add671d2025-01-22T07:11:01ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-01-011510.3389/fmicb.2024.15166671516667Deep learning in microbiome analysis: a comprehensive review of neural network modelsPiotr Przymus0Krzysztof Rykaczewski1Adrián Martín-Segura2Jaak Truu3Enrique Carrillo De Santa Pau4Mikhail Kolev5Mikhail Kolev6Irina Naskinova7Aleksandra Gruca8Alexia Sampri9Alexia Sampri10Marcus Frohme11Alina Nechyporenko12Alina Nechyporenko13Faculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń, Toruń, Pomeranian, PolandFaculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń, Toruń, Pomeranian, PolandComputational Biology Group, IMDEA Food Institute, Madrid, SpainInstitute of Molecular and Cell Biology, University of Tartu, Tartu, EstoniaComputational Biology Group, IMDEA Food Institute, Madrid, SpainDepartment of Mathematics, University of Architecture, Civil Engineering and Geodesy, Sofia, BulgariaDepartment of Applied Computer Science and Mathematical Modeling, Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Olsztyn, PolandDepartment of Mathematics, University of Architecture, Civil Engineering and Geodesy, Sofia, BulgariaDepartment of Computer Networks and Systems, Silesian University of Technology, Gliwice, PolandBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United KingdomVictor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United KingdomMolecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Brandenburg, GermanyMolecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Brandenburg, Germany0Department of System Engineering, Kharkiv National University of Radioelectronics, Kharkiv, UkraineMicrobiome 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.https://www.frontiersin.org/articles/10.3389/fmicb.2024.1516667/fullmicrobiomedeep learningclassificationnatural language processingclustering |
| spellingShingle | Piotr Przymus Krzysztof Rykaczewski Adrián Martín-Segura Jaak Truu Enrique Carrillo De Santa Pau Mikhail Kolev Mikhail Kolev Irina Naskinova Aleksandra Gruca Alexia Sampri Alexia Sampri Marcus Frohme Alina Nechyporenko Alina Nechyporenko Deep learning in microbiome analysis: a comprehensive review of neural network models Frontiers in Microbiology microbiome deep learning classification natural language processing clustering |
| title | Deep learning in microbiome analysis: a comprehensive review of neural network models |
| title_full | Deep learning in microbiome analysis: a comprehensive review of neural network models |
| title_fullStr | Deep learning in microbiome analysis: a comprehensive review of neural network models |
| title_full_unstemmed | Deep learning in microbiome analysis: a comprehensive review of neural network models |
| title_short | Deep learning in microbiome analysis: a comprehensive review of neural network models |
| title_sort | deep learning in microbiome analysis a comprehensive review of neural network models |
| topic | microbiome deep learning classification natural language processing clustering |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1516667/full |
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