The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Bioinformatics Core Luxembourg Centre for Systems Biomedicine University of Luxembourg Esch sur Alzette Luxembourg

Bioinformatics Research Laboratory Department of Biological Sciences University of Cyprus Nicosia Cyprus

Bioinformatics Research Unit Riga Stradins University Riga Latvia

Biotechnical Faculty University of Ljubljana Ljubljana Slovenia

Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición Instituto de Salud Carlos 3 Madrid Spain

Centro de Matemática e Aplicações FCT UNL Caparica Portugal

CINTESIS NOVA Medical School NMS Universidade Nova de Lisboa Lisbon Portugal

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

Computational Oncology Sage Bionetworks Seattle WA United States

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

Department of Biology University of Fribourg Fribourg Switzerland

Department of Clinical Science University of Bergen Bergen Norway

Department of Computer Engineering Abdullah Gul University Kayseri Turkey

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

Department of Computer Science University of Bari Aldo Moro Bari Italy

Department of Computer Technologies Karadeniz Technical University Trabzon Turkey

Department of Computing University of Turku Turku Finland

Department of Electrical and Electronics Engineering Karadeniz Technical University Trabzon Turkey

Department of Epidemiology Erasmus Medical Center Rotterdam Netherlands

Department of Genetics and Bioengineering International University of Sarajevo Sarajevo Bosnia and Herzegovina

Department of Infection and Immunity Luxembourg Institute of Health Esch sur Alzette Luxembourg

Department of Infectious Diseases and Immunology Faculty of Veterinary Medicine Utrecht University Utrecht Netherlands

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

Department of Microbiology and Clinical Microbiology Faculty of Medicine Erciyes University Kayseri Turkey

Department of Microbiology University of Innsbruck Innsbruck Austria

Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester Manchester United Kingdom

European Molecular Biology Laboratory Structural and Computational Biology Unit Heidelberg Germany

Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia

Faculty of Information Tehnology and Bionics Pázmány University Budapest Hungary

Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruñ Poland

Human Genetics and Disease Mechanisms Latvian Biomedical Research and Study Centre Riga Latvia

Institute of Computational Biomedicine Heidelberg University Faculty of Medicine and Heidelberg University Hospital Heidelberg Germany

Institute of Molecular and Cell Biology University of Tartu Tartu Estonia

Instituto de Investigación Biomédica de Málaga Unidad de Gestión Clìnica de Endocrinologìa y Nutrición Hospital Clìnico Universitario Virgen de la Victoria Universidad de Málaga Málaga Spain

Jozef Stefan Institute Ljubljana Slovenia

Laboratory of Genetics Department of Biotechnology School of Applied Biology and Biotechnology Agricultural University of Athens Athens Greece

Latvian Biomedical Research and Study Centre Riga Latvia

Metagenomics Laboratory Genome and Stem Cell Center Erciyes University Kayseri Turkey

Molecular Nutrition and Proteomics Lab Faculty of the Food Science and Technology Institute of Life Sciences University of Agricultural Sciences and Veterinary Medicine of Cluj Napoca Cluj Napoca Romania

Navarrabiomed Complejo Hospitalario de Navarra Pamplona Spain

NOVA Laboratory for Computer Science and Informatics FCT UNL Caparica Portugal

Odense Research Center for Anaphylaxis Department of Dermatology and Allergy Center Odense University Hospital University of Southern Denmark Odense Denmark

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

School of Microbiology and APC Microbiome Ireland University College Cork Cork Ireland

Swiss Institute of Bioinformatics Lausanne Switzerland

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

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

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment