@article{PrzymusRykaczewskiMartinSeguraetal.2025, author = {Przymus, Piotr and Rykaczewski, Krzysztof and Mart{\´i}n-Segura, Adri{\´a}n and Truu, Jaak and De Santa Pau, Enrique Carrillo and Kolev, Mikhail and Naskinova, Irina and Gruca, Aleksandra and Sampri, Alexia and Frohme, Marcus and Nechyporenko, Alina}, title = {Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models}, series = {Frontiers in Microbiology}, volume = {15}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19889}, year = {2025}, abstract = {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.}, language = {en} } @article{D'EliaTruuLahtietal.2023, author = {D'Elia, Domenica and Truu, Jaak and Lahti, Leo and Berland, Magali and Papoutsoglou, Georgios and Ceci, Michelangelo and Zomer, Aldert and Lopes, Marta B. and Ibrahimi, Eliana and Gruca, Aleksandra and Nechyporenko, Alina and Frohme, Marcus and Klammsteiner, Thomas and Carrillo de Santa Pau, Enrique and Marcos-Zambrano, Laura Judith and Hron, Karel and Pio, Gianvito and Simeon, Andrea and Suharoschi, Ramona and Moreno-Indias, Isabel and Temko, Andriy and Nedyalkova, Miroslava and Apostol, Elena-Simona and Truică, Ciprian-Octavian and Shigdel, Rajesh and Telalović, Jasminka Hasić and Bongcam-Rudloff, Erik and Przymus, Piotr and Jordamović, Naida Babić and Falquet, Laurent and Tarazona, Sonia and Sampri, Alexia and Isola, Gaetano and P{\´e}rez-Serrano, David and Trajkovik, Vladimir and Klucar, Lubos and Loncar-Turukalo, Tatjana and Havulinna, Aki S. and Jansen, Christian and Bertelsen, Randi J. and Claesson, Marcus Joakim}, title = {Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action}, series = {Frontiers in Microbiology}, volume = {14}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18004}, year = {2023}, abstract = {The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.}, language = {en} }