Frontiers in Microbiology (Sep 2023)
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
- Domenica D’Elia,
- Jaak Truu,
- Leo Lahti,
- Magali Berland,
- Georgios Papoutsoglou,
- Georgios Papoutsoglou,
- Michelangelo Ceci,
- Aldert Zomer,
- Marta B. Lopes,
- Marta B. Lopes,
- Eliana Ibrahimi,
- Aleksandra Gruca,
- Alina Nechyporenko,
- Alina Nechyporenko,
- Marcus Frohme,
- Thomas Klammsteiner,
- 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,
- Naida Babić Jordamović,
- Laurent Falquet,
- Sonia Tarazona,
- Alexia Sampri,
- Alexia Sampri,
- Gaetano Isola,
- David Pérez-Serrano,
- Vladimir Trajkovik,
- Lubos Klucar,
- Tatjana Loncar-Turukalo,
- Aki S. Havulinna,
- Aki S. Havulinna,
- Christian Jansen,
- Christian Jansen,
- Randi J. Bertelsen,
- Marcus Joakim Claesson
Affiliations
- Domenica D’Elia
- Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy
- Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
- Magali Berland
- Université Paris-Saclay, INRAE, MetaGenoPolis, Jouy-en-Josas, France
- Georgios Papoutsoglou
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
- Georgios Papoutsoglou
- Department of Computer Science, University of Crete, Heraklion, Greece
- Michelangelo Ceci
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
- Aldert Zomer
- Department of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
- Marta B. Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- Marta B. Lopes
- 0UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
- Eliana Ibrahimi
- 1Department of Biology, University of Tirana, Tirana, Albania
- Aleksandra Gruca
- 2Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
- Alina Nechyporenko
- 3Systems Engineering Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
- Alina Nechyporenko
- 4Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
- Marcus Frohme
- 4Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
- Thomas Klammsteiner
- 5Department of Microbiology, Universität Innsbruck, Innsbruck, Austria
- Thomas Klammsteiner
- 6Department of Ecology, Universität Innsbruck, Innsbruck, Austria
- Enrique Carrillo-de Santa Pau
- 7Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
- Laura Judith Marcos-Zambrano
- 7Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
- Karel Hron
- 8Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, Olomouc, Czechia
- Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
- Andrea Simeon
- 9BioSense Institute, University of Novi Sad, Novi Sad, Serbia
- Ramona Suharoschi
- 0Molecular Nutrition and Proteomics Research Laboratory, Department of Food Science, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
- Isabel Moreno-Indias
- 1Department 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
- Andriy Temko
- 2Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
- Miroslava Nedyalkova
- 3Chemistry and Pharmacy Department, University of Sofia, Sofia, Bulgaria
- Elena-Simona Apostol
- 4Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
- Ciprian-Octavian Truică
- 4Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
- Rajesh Shigdel
- 5Department of Clinical Science, University of Bergen, Bergen, Norway
- Jasminka Hasić Telalović
- 6Department of Computer Science, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
- Erik Bongcam-Rudloff
- 7Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden
- Piotr Przymus
- 8Nicolaus Copernicus University Torun, Torun, Poland
- Naida Babić Jordamović
- 9Computational Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Naida Babić Jordamović
- 0Verlab Research Institute for BIomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina
- Laurent Falquet
- 1University of Fribourg and Swiss Institute of Bioinformatics, Fribourg, Switzerland
- Sonia Tarazona
- 2Department of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València, València, Spain
- Alexia Sampri
- 3British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Alexia Sampri
- 4Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- Gaetano Isola
- 5Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
- David Pérez-Serrano
- 7Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
- Vladimir Trajkovik
- 6Ss. Cyril and Methodius University, Skopje, North Macedonia
- Lubos Klucar
- 7Institute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovakia
- Tatjana Loncar-Turukalo
- 8Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
- Aki S. Havulinna
- 9Finnish Institute for Health and Welfare, Helsinki, Finland
- Aki S. Havulinna
- 0Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
- Christian Jansen
- 1Biome Diagnostics GmbH, Vienna, Austria
- Christian Jansen
- 2Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
- Randi J. Bertelsen
- 3University of Bergen, Bergen, Norway
- Marcus Joakim Claesson
- 4School of Microbiology & APC Microbiome Ireland, University College Cork, Cork, Ireland
- DOI
- https://doi.org/10.3389/fmicb.2023.1257002
- Journal volume & issue
-
Vol. 14
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.
Keywords
