Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action(Article)(Open Access)
- aDepartment of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy
- bInstitute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- cDepartment of Computing, University of Turku, Turku, Finland
- dUniversité Paris-Saclay, INRAE, MetaGenoPolis, Jouy-en-Josas, France
- eJADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
- fDepartment of Computer Science, University of Crete, Heraklion, Greece
- gDepartment of Computer Science, University of Bari Aldo Moro, Bari, Italy
- hDepartment of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
- iCenter for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- jUNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
- kDepartment of Biology, University of Tirana, Tirana, Albania
- lDepartment of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
- mSystems Engineering Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
- nDepartment of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
- oDepartment of Microbiology, Universität Innsbruck, Innsbruck, Austria
- pDepartment of Ecology, Universität Innsbruck, Innsbruck, Austria
- qComputational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
- rDepartment of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, Olomouc, Czech Republic
- sBioSense Institute, University of Novi Sad, Novi Sad, Serbia
- tMolecular Nutrition and Proteomics Research Laboratory, Department of Food Science, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
- uDepartment 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
- vDepartment of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
- wChemistry and Pharmacy Department, University of Sofia, Sofia, Bulgaria
- xComputer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
- yDepartment of Clinical Science, University of Bergen, Bergen, Norway
- zDepartment of Computer Science, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
- aaSwedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden
- abNicolaus Copernicus University Torun, Torun, Poland
- acComputational Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- adVerlab Research Institute for BIomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina
- aeUniversity of Fribourg and Swiss Institute of Bioinformatics, Fribourg, Switzerland
- afDepartment of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València, València, Spain
- agBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- ahVictor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- aiDepartment of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
- ajSs. Cyril and Methodius University, Skopje, North Macedonia
- akInstitute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovakia
- alFaculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
- amFinnish Institute for Health and Welfare, Helsinki, Finland
- anInstitute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
- aoBiome Diagnostics GmbH, Vienna, Austria
- apInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
- aqUniversity of Bergen, Bergen, Norway
- arSchool of Microbiology & APC Microbiome Ireland, University College Cork, Cork, Ireland
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. Copyright © 2023 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson.
Indexed keywords
| EMTREE medical terms: | achievementarticleartificial intelligenceautomationcontrolled studygold standardhealth care practicehumanhuman experimentmachine learningmicrobiomenonhumanpersonalized medicine |
|---|---|
Funding details
| Funding sponsor | Funding number | Acronym |
|---|---|---|
| Federación Española de Enfermedades Raras | FEDER | |
| ANR-11-DPBS-0001 | ||
| European Cooperation in Science and Technology | CPII21/00013,CA18131 | COST |
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1
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is based upon work from COST Action ML4Microbiome \u201CStatistical and machine learning techniques in human microbiome studies\u201D (CA18131), supported by COST (European Cooperation in Science and Technology), www.cost.eu . MB acknowledges support through the Metagenopolis grant ANR-11-DPBS-0001. IM-I acknowledges support by the \u201CMiguel Servet Type II\u201D program (CPII21/00013) of the ISCIII-Madrid (Spain), co-financed by the FEDER.
- ISSN: 1664302X
- Source Type: Journal
- Original language: English
- DOI: 10.3389/fmicb.2023.1257002
- Document Type: Article
- Publisher: Frontiers Media SA
D’Elia, D.; Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy;
© Copyright 2023 Elsevier B.V., All rights reserved.
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