Advancing microbiome research with machine learning - key findings from the ML4Microbiome COST action

Aug 2, 2026

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Advancing microbiome research with machine learning highlights key findings from the ML4Microbiome COST action, presenting collaborative academic work on computational methods for microbiome study. The publication emphasizes open-access dissemination and formal scholarly review, detailing contributions across multiple international institutions and research groups. It frames how machine learning can support microbiome data analysis to derive actionable insights, while providing a citable record for academic use under Creative Commons Attribution terms.

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Reading Time20 min

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TYPE Perspective PUBLISHED 25 September 2023 DOI 10.3389/fmicb.2023.1257002 OPEN ACCESS EDITED BY Richard Allen White III, University of North Carolina at Charlotte, United States REVIEWED BY Himel Mallick, Cornell University, United States *CORRESPONDENCE Domenica D’Elia [domenica.delia@cnr.it](domenica.delia@cnr.it) RECEIVED 11 July 2023 ACCEPTED 05 September 2023 PUBLISHED 25 September 2023 CITATION D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau EC-dS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol E-S, Truică C-O, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ and Claesson MJ (2023) Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front. Microbiol. 14:1257002 . doi: 10.3389/fmicb.2023.1257002 COPYRIGHT © 2023 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action Domenica D’Elia 1*, Jaak Truu 2, Leo Lahti3, Magali Berland4, Georgios Papoutsoglou 5, 6, Michelangelo Ceci7, Aldert Zomer 8, Marta B. Lopes 9, 10, Eliana Ibrahimi 11, Aleksandra Gruca 12, Alina Nechyporenko 13, 14, Marcus Frohme 14, Thomas Klammsteiner 15, 16, Enrique Carrillo-de Santa Pau 17, Laura Judith Marcos-Zambrano 17, Karel Hron 18, Gianvito Pio7, Andrea Simeon 19, Ramona Suharoschi 20, Isabel Moreno-Indias 21, Andriy Temko 22, Miroslava Nedyalkova 23, Elena-Simona Apostol 24, Ciprian-Octavian Truică 24, Rajesh Shigdel 25, Jasminka Hasić Telalović 26, Erik Bongcam-Rudloff 27, Piotr Przymus 28, Naida Babić Jordamović 29, 30, Laurent Falquet31, Sonia Tarazona32, Alexia Sampri33, 34, Gaetano Isola35, David Pérez-Serrano 17, Vladimir Trajkovik36, Lubos Klucar37, Tatjana Loncar-Turukalo38, Aki S. Havulinna39, 40, Christian Jansen41, 42, Randi J. Bertelsen43 and Marcus Joakim Claesson44 1 Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy, 2 Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia, 3 Department of Computing, University of Turku, Turku, Finland, 4 Université Paris-Saclay, INRAE, MetaGenoPolis, Jouyen-Josas, France, 5JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece, 6 Department of Computer Science, University of Crete, Heraklion, Greece, 7 Department of Computer Science, University of Bari Aldo Moro, Bari, Italy, 8 Department of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands, 9Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal, 10 UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal, 11 Department of Biology, U

FAQs

What is the focus of the document?

It focuses on advancing microbiome research using machine learning, summarizing key findings from the ML4Microbiome COST action.

Who are the main contributors?

The document lists multiple authors, led by Domenica D’Elia, with affiliations spanning many international institutions.

Is the work open access and how can it be used?

Yes. It is open access under a Creative Commons Attribution license (CC BY), with reuse permitted when the original authors and source are credited.

Aug 2, 2026

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