AUTHOR=D’Elia Domenica , Truu Jaak , Lahti Leo , Berland Magali , Papoutsoglou Georgios , Ceci Michelangelo , Zomer Aldert , Lopes Marta B. , Ibrahimi Eliana , Gruca Aleksandra , Nechyporenko Alina , Frohme Marcus , Klammsteiner Thomas , Pau Enrique Carrillo-de Santa , Marcos-Zambrano Laura Judith , Hron Karel , Pio Gianvito , Simeon Andrea , Suharoschi Ramona , Moreno-Indias Isabel , Temko Andriy , Nedyalkova Miroslava , Apostol Elena-Simona , Truică Ciprian-Octavian , Shigdel Rajesh , Telalović Jasminka Hasić , Bongcam-Rudloff Erik , Przymus Piotr , Jordamović Naida Babić , Falquet Laurent , Tarazona Sonia , Sampri Alexia , Isola Gaetano , Pérez-Serrano David , Trajkovik Vladimir , Klucar Lubos , Loncar-Turukalo Tatjana , Havulinna Aki S. , Jansen Christian , Bertelsen Randi J. , Claesson Marcus Joakim TITLE=Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action JOURNAL=Frontiers in Microbiology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1257002 DOI=10.3389/fmicb.2023.1257002 ISSN=1664-302X 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.