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Autor
Apostol, Elena-Simona 1 Berland, Magali 1 Bertelsen, Randi J 1 Bongcam-Rudloff, Erik 1 Ceci, Michelangelo 1 Claesson, Marcus Joakim 1 D'Elia, Domenica 1 Falquet, Laurent 1 Frohme, Marcus 1 Gruca, Aleksandra 1 Havulinna, Aki S 1 Hron, Karel 1 Ibrahimi, Eliana 1 Isola, Gaetano 1 Jansen, Christian 1 Jordamović, Naida Babić 1 Klammsteiner, Thomas 1 Klucar, Lubos 1 Lahti, Leo 1 Loncar-Turukalo, Tatjana 1
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Pracoviště
BioSense Institute University of Novi... 1 Biome Diagnostics GmbH Vienna Austria 1 British Heart Foundation Cardiovascul... 1 Center for Mathematics and Applicatio... 1 Chemistry and Pharmacy Department Uni... 1 Computational Biology Group Precision... 1 Computational Biology International C... 1 Computer Science and Engineering Depa... 1 Department of Applied Statistics and ... 1 Department of Biology University of T... 1 Department of Biomedical Sciences Nat... 1 Department of Biomolecular Health Sci... 1 Department of Clinical Science Univer... 1 Department of Computer Networks and S... 1 Department of Computer Science Univer... 1 Department of Computer Science Univer... 1 Department of Computer Science Univer... 1 Department of Computing University of... 1 Department of Ecology Universität Inn... 1 Department of Electrical and Electron... 1
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Autor
Apostol, Elena-Simona 1 Berland, Magali 1 Bertelsen, Randi J 1 Bongcam-Rudloff, Erik 1 Ceci, Michelangelo 1 Claesson, Marcus Joakim 1 D'Elia, Domenica 1 Falquet, Laurent 1 Frohme, Marcus 1 Gruca, Aleksandra 1 Havulinna, Aki S 1 Hron, Karel 1 Ibrahimi, Eliana 1 Isola, Gaetano 1 Jansen, Christian 1 Jordamović, Naida Babić 1 Klammsteiner, Thomas 1 Klucar, Lubos 1 Lahti, Leo 1 Loncar-Turukalo, Tatjana 1
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Pracoviště
BioSense Institute University of Novi... 1 Biome Diagnostics GmbH Vienna Austria 1 British Heart Foundation Cardiovascul... 1 Center for Mathematics and Applicatio... 1 Chemistry and Pharmacy Department Uni... 1 Computational Biology Group Precision... 1 Computational Biology International C... 1 Computer Science and Engineering Depa... 1 Department of Applied Statistics and ... 1 Department of Biology University of T... 1 Department of Biomedical Sciences Nat... 1 Department of Biomolecular Health Sci... 1 Department of Clinical Science Univer... 1 Department of Computer Networks and S... 1 Department of Computer Science Univer... 1 Department of Computer Science Univer... 1 Department of Computer Science Univer... 1 Department of Computing University of... 1 Department of Ecology Universität Inn... 1 Department of Electrical and Electron... 1
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Free Medical Journals od 2010
PubMed Central od 2010
Europe PubMed Central od 2010
Open Access Digital Library od 2010-01-01
Open Access Digital Library od 2010-01-01
ROAD: Directory of Open Access Scholarly Resources od 2010
PubMed
37808321
DOI
10.3389/fmicb.2023.1257002
Knihovny.cz E-zdroje
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.
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Po ukončení testovacího provozu bude odkaz přesměrován adresu produkční verze portálu Medvik.