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

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

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 medic...

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Bibliographic Details
Main Authors: Domenica D’Elia, Jaak Truu, Leo Lahti, Magali Berland, Georgios Papoutsoglou, Michelangelo Ceci, Aldert Zomer, Marta B. Lopes, Eliana Ibrahimi, Aleksandra Gruca, Alina Nechyporenko, Marcus Frohme, Thomas Klammsteiner, Enrique Carrillo-de Santa Pau, Laura Judith Marcos-Zambrano, Karel Hron, Gianvito Pio, Andrea Simeon, Ramona Suharoschi, Isabel Moreno-Indias, Andriy Temko, Miroslava Nedyalkova, Elena-Simona Apostol, Ciprian-Octavian Truică, Rajesh Shigdel, Jasminka Hasić Telalović, Erik Bongcam-Rudloff, Piotr Przymus, Naida Babić Jordamović, Laurent Falquet, Sonia Tarazona, Alexia Sampri, Gaetano Isola, David Pérez-Serrano, Vladimir Trajkovik, Lubos Klucar, Tatjana Loncar-Turukalo, Aki S. Havulinna, Christian Jansen, Randi J. Bertelsen, Marcus Joakim Claesson
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-09-01
Series:Frontiers in Microbiology
Subjects:
microbiome
machine learning
artificial intelligence
standards
best practices
Online Access:https://www.frontiersin.org/articles/10.3389/fmicb.2023.1257002/full

Internet

https://www.frontiersin.org/articles/10.3389/fmicb.2023.1257002/full

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