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

British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom

Center for Mathematics and Applications NOVA School of Science and Technology Caparica Portugal

Chemistry and Pharmacy Department University of Sofia Sofia Bulgaria

Computational Biology Group Precision Nutrition and Cancer Research Program IMDEA Food Institute CEI UAM CSIC Madrid Spain

Computational Biology International Centre for Genetic Engineering and Biotechnology Trieste Italy

Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Department of Applied Statistics and Operations Research and Quality Universitat Politècnica de València València Spain

Department of Biology University of Tirana Tirana Albania

Department of Biomedical Sciences National Research Council Institute for Biomedical Technologies Bari Italy

Department of Biomolecular Health Sciences Faculty of Veterinary Medicine Utrecht University Utrecht Netherlands

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 Computer Science University Sarajevo School of Science and Technology Sarajevo Bosnia and Herzegovina

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 Endocrinology and Nutrition Virgen de la Victoria University Hospital the Biomedical Research Institute of Malaga and Platform in Nanomedicine University of Malaga Malaga Spain

Department of General Surgery and Surgical Medical Specialties School of Dentistry University of Catania Catania Italy

Department of Mathematical Analysis and Applications of Mathematics Faculty of Science Palacký University Olomouc Czechia

Department of Microbiology Universität Innsbruck Innsbruck Austria

Department of Molecular Biotechnology and Functional Genomics Technical University of Applied Sciences Wildau Wildau Germany

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

Molecular Nutrition and Proteomics Research Laboratory Department of Food Science University of Agricultural Sciences and Veterinary Medicine of Cluj Napoca Cluj Napoca Romania

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

Swedish University of Agricultural Sciences Department of Animal Breeding and Genetics Uppsala Sweden

Systems Engineering Department Kharkiv National University of Radio Electronics Kharkiv Ukraine

UNIDEMI Department of Mechanical and Industrial Engineering NOVA School of Science and Technology Caparica Portugal

Université Paris Saclay INRAE MetaGenoPolis Jouy en Josas France

University of Bergen Bergen Norway

University of Fribourg and Swiss Institute of Bioinformatics Fribourg Switzerland

Verlab Research Institute for BIomedical Engineering Medical Devices and Artificial Intelligence Sarajevo Bosnia and Herzegovina

Victor Phillip Dahdaleh Heart and Lung Research Institute University of Cambridge Cambridge United Kingdom

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Overview of data preprocessing for machine learning applications in human microbiome research