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dc.contributor.authorD’Elia, Domenica
dc.contributor.authorTruu, Jaak
dc.contributor.authorLahti, Leo
dc.contributor.authorBerland, Magali
dc.contributor.authorPapoutsoglou, Georgios
dc.contributor.authorCeci, Michelangelo
dc.contributor.authorZomer, Aldert
dc.contributor.authorLopes, Marta B.
dc.contributor.authorIbrahimi, Eliana
dc.contributor.authorGruca, Aleksandra
dc.contributor.authorNechyporenko, Alina
dc.contributor.authorFrohme, Marcus
dc.contributor.authorKlammsteiner, Thomas
dc.contributor.authorPau, Enrique Carrillo-de Santa
dc.contributor.authorMarcos-Zambrano, Laura Judith
dc.contributor.authorHron, Karel
dc.contributor.authorPio, Gianvito
dc.contributor.authorSimeon, Andrea
dc.contributor.authorSuharoschi, Ramona
dc.contributor.authorMoreno-Indias, Isabel
dc.contributor.authorTemko, Andriy
dc.contributor.authorNedyalkova, Miroslava
dc.contributor.authorApostol, Elena-Simona
dc.contributor.authorTruică, Ciprian-Octavian
dc.contributor.authorShigdel, Rajesh
dc.contributor.authorTelalović, Jasminka Hasić
dc.contributor.authorBongcam-Rudloff, Erik
dc.contributor.authorPrzymus, Piotr
dc.contributor.authorJordamović, Naida Babić
dc.contributor.authorFalquet, Laurent
dc.contributor.authorTarazona, Sonia
dc.contributor.authorSampri, Alexia
dc.contributor.authorIsola, Gaetano
dc.contributor.authorPérez-Serrano, David
dc.contributor.authorTrajkovik, Vladimir
dc.contributor.authorKlucar, Lubos
dc.contributor.authorLoncar-Turukalo, Tatjana
dc.contributor.authorHavulinna, Aki S.
dc.contributor.authorJansen, Christian
dc.contributor.authorBertelsen, Randi Jacobsen
dc.contributor.authorClaesson, Marcus Joakim
dc.date.accessioned2024-08-01T08:04:34Z
dc.date.available2024-08-01T08:04:34Z
dc.date.created2023-11-09T12:12:50Z
dc.date.issued2023
dc.identifier.issn1664-302X
dc.identifier.urihttps://hdl.handle.net/11250/3143990
dc.description.abstractThe 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.en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAdvancing microbiome research with machine learning: key findings from the ML4Microbiome COST actionen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.articlenumber1257002en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.3389/fmicb.2023.1257002
dc.identifier.cristin2194505
dc.source.journalFrontiers in Microbiologyen_US
dc.identifier.citationFrontiers in Microbiology. 2023, 14, 1257002.en_US
dc.source.volume14en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal