• Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions 

      Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava; Elbere, Ilze; Roshchupkin, Gennady V.; Adilovic, Muhamed; Aydemir, Onder; Bakir-Gungor, Burcu; Carrillo-de Santa Pau, Enrique; D’Elia, Domenica; Desai, Mahesh S.; Falquet, Laurent; Gundogdu, Aycan; Hron, Karel; Klammsteiner, Thomas; Lopes, Marta B.; Marcos-Zambrano, Laura Judith; Marques, Cláudia; Mason, Michael; May, Patrick; Pašić, Lejla; Pio, Gianvito; Pongor, Sándor; Promponas, Vasilis J.; Przymus, Piotr; Saez-Rodriguez, Julio; Sampri, Alexia; Shigdel, Rajesh; Stres, Blaz; Suharoschi, Ramona; Truu, Jaak; Truică, Ciprian-Octavian; Vilne, Baiba; Vlachakis, Dimitrios; Yilmaz, Ercument; Zeller, Georg; Zomer, Aldert L.; Gomez-Cabrero, David; Claesson, Marcus J. (Journal article; Peer reviewed, 2021)
      The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many ...