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

      D’Elia, Domenica; Truu, Jaak; Lahti, Leo; Berland, Magali; Papoutsoglou, Georgios; Ceci, Michelangelo; Zomer, Aldert; Lopes, Marta B.; Ibrahimi, Eliana; Gruca, Aleksandra; Nechyporenko, Alina; Frohme, Marcus; Klammsteiner, Thomas; Pau, Enrique Carrillo-de Santa; Marcos-Zambrano, Laura Judith; Hron, Karel; Pio, Gianvito; Simeon, Andrea; Suharoschi, Ramona; Moreno-Indias, Isabel; Temko, Andriy; Nedyalkova, Miroslava; Apostol, Elena-Simona; Truică, Ciprian-Octavian; Shigdel, Rajesh; Telalović, Jasminka Hasić; Bongcam-Rudloff, Erik; Przymus, Piotr; Jordamović, Naida Babić; Falquet, Laurent; Tarazona, Sonia; Sampri, Alexia; Isola, Gaetano; Pérez-Serrano, David; Trajkovik, Vladimir; Klucar, Lubos; Loncar-Turukalo, Tatjana; Havulinna, Aki S.; Jansen, Christian; Bertelsen, Randi Jacobsen; Claesson, Marcus Joakim (Journal article; Peer reviewed, 2023)
      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 ...