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Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
D. D'Elia, J. Truu, L. Lahti, M. Berland, G. Papoutsoglou, M. Ceci, A. Zomer, MB. Lopes, E. Ibrahimi, A. Gruca, A. Nechyporenko, M. Frohme, T. Klammsteiner, ECS. Pau, LJ. Marcos-Zambrano, K. Hron, G. Pio, A. Simeon, R. Suharoschi, I....
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Document type Journal Article
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- Journal Article MeSH
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.
Biome Diagnostics GmbH Vienna Austria
BioSense Institute University of Novi Sad Novi Sad Serbia
Center for Mathematics and Applications NOVA School of Science and Technology Caparica Portugal
Chemistry and Pharmacy Department University of Sofia Sofia Bulgaria
Computational Biology International Centre for Genetic Engineering and Biotechnology Trieste Italy
Department of Biology University of Tirana Tirana Albania
Department of Clinical Science University of Bergen Bergen Norway
Department of Computer Networks and Systems Silesian University of Technology Gliwice Poland
Department of Computer Science University of Bari Aldo Moro Bari Italy
Department of Computer Science University of Crete Heraklion Greece
Department of Computing University of Turku Turku Finland
Department of Ecology Universität Innsbruck Innsbruck Austria
Department of Electrical and Electronic Engineering University College Cork Cork Ireland
Department of Microbiology Universität Innsbruck Innsbruck Austria
Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia
Finnish Institute for Health and Welfare Helsinki Finland
Institute for Molecular Medicine Finland FIMM HiLIFE Helsinki Finland
Institute of Molecular and Cell Biology University of Tartu Tartu Estonia
Institute of Molecular Biology Slovak Academy of Sciences Bratislava Slovakia
Institute of Science and Technology Austria Klosterneuburg Austria
JADBio Gnosis DA S A Science and Technology Park of Crete Heraklion Greece
Nicolaus Copernicus University Torun Torun Poland
School of Microbiology and APC Microbiome Ireland University College Cork Cork Ireland
Ss Cyril and Methodius University Skopje North Macedonia
Systems Engineering Department Kharkiv National University of Radio Electronics Kharkiv Ukraine
Université Paris Saclay INRAE MetaGenoPolis Jouy en Josas France
University of Bergen Bergen Norway
University of Fribourg and Swiss Institute of Bioinformatics Fribourg Switzerland
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