Editorial: Microbiome and machine learning, volume II

Domenica D'Elia; Aldert Zomer; Isabel Moreno Indias; Erik Bongcam-Rudloff; Randi Jacobsen Bertelsen; Marcus Joakim Claesson

doi:10.3389/fmicb.2024.1499260

Editorial: Microbiome and machine learning, volume II

Front Microbiol. 2024 Oct 15:15:1499260. doi: 10.3389/fmicb.2024.1499260. eCollection 2024.

Authors

Domenica D'Elia¹, Aldert Zomer², Isabel Moreno Indias³, Erik Bongcam-Rudloff⁴, Randi Jacobsen Bertelsen⁵, Marcus Joakim Claesson⁶

Affiliations

¹ Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy.
² Department of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands.
³ Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, The Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain.
⁴ Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden.
⁵ Department of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway.
⁶ School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland.

No abstract available

Keywords: best practices; explainable artificial intelligence; machine learning; microbiome; standards.

Publication types

Editorial

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors are grateful to all members of COST Action CA18131, Statistical and machine learning techniques in human microbiome studies for their contribution to the COST Action objectives and to COST (European Cooperation in Science and Technology) for the economic support (www.cost.eu).