A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
Tekijät: 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 ECS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol ES, Truică CO, 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, Claesson MJ
Kustantaja: Frontiers Media S.A.
Julkaisuvuosi: 2023
Journal: Frontiers in Microbiology
Tietokannassa oleva lehden nimi: Frontiers in microbiology
Lehden akronyymi: Front Microbiol
Artikkelin numero: 1270488
Vuosikerta: 14
ISSN: 1664-302X
DOI: https://doi.org/10.3389/fmicb.2023.1257002
Verkko-osoite: https://doi.org/10.3389/fmicb.2023.1257002
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181581353
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
