Piotr Przymus ¹ Krzysztof Rykaczewski ¹ Adrián Martín-Segura ^2* Jaak Truu ³ Enrique Carrillo De Santa Pau ² Mikhail Kolev ^4,5 Irina Naskinova ⁴ Aleksandra Gruca ⁶ Alexia Sampri ^7,8 Marcus Frohme ⁹ Alina Nechyporenko ^10,9

• ¹ Nicolaus Copernicus University in Toruń, Toruń, Pomeranian, Poland
• ² IMDEA Food Institute, Madrid, Madrid, Spain
• ³ University of Tartu, Tartu, Tartu County, Estonia
• ⁴ University of Architecture, Civil Engineering and Geodesy, Sofia, Sofia City, Bulgaria
• ⁵ University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian, Poland
• ⁶ Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Silesian, Poland
• ⁷ Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom
• ⁸ Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
• ⁹ Technical University of Applied Sciences Wildau, Wildau, Brandenburg, Germany
• ¹⁰ Kharkiv National University of Radioelectronics, Ukraine, Ukraine

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.