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

  • 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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Author:Domenica D'EliaORCiD, Jaak TruuORCiD, Leo LahtiORCiD, Magali BerlandORCiD, Georgios PapoutsoglouORCiD, Michelangelo CeciORCiD, Aldert ZomerORCiD, Marta B. LopesORCiD, Eliana IbrahimiORCiD, Aleksandra Gruca, Alina NechyporenkoORCiD, Marcus FrohmeORCiDGND, Thomas KlammsteinerORCiD, Enrique Carrillo de Santa PauORCiD, Laura Judith Marcos-ZambranoORCiD, Karel HronORCiD, Gianvito PioORCiD, Andrea SimeonORCiD, Ramona SuharoschiORCiD, Isabel Moreno-IndiasORCiD, Andriy TemkoORCiD, Miroslava NedyalkovaORCiD, Elena-Simona ApostolORCiD, Ciprian-Octavian TruicăORCiD, Rajesh ShigdelORCiD, Jasminka Hasić TelalovićORCiD, Erik Bongcam-RudloffORCiD, Piotr PrzymusORCiD, Naida Babić JordamovićORCiD, Laurent FalquetORCiD, Sonia TarazonaORCiD, Alexia SampriORCiD, Gaetano IsolaORCiD, David Pérez-Serrano, Vladimir TrajkovikORCiD, Lubos KlucarORCiD, Tatjana Loncar-TurukaloORCiD, Aki S. HavulinnaORCiD, Christian Jansen, Randi J. Bertelsen, Marcus Joakim ClaessonORCiD
URN:urn:nbn:de:kobv:526-opus4-18004
DOI (Version of Record):https://doi.org/10.3389/fmicb.2023.1257002
ISSN:1664-302X
Parent Title (English):Frontiers in Microbiology
Publisher:Frontiers
Document Type:Article
Language:English
Year of Publication:2023
Publishing Institution:Technische Hochschule Wildau
Release Date:2023/10/02
Tag:artificial intelligence; best practices; machine learning; microbiome; standards
Volume:14
Article Number:1257002
Source:D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau EC-dS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol E-S, Truică C-O, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ and Claesson MJ (2023) Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front. Microbiol. 14:1257002. doi: 10.3389/fmicb.2023.1257002
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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