TY  - JOUR
AU  - Marcos-Zambrano, Laura Judith
AU  - Karaduzovic-Hadziabdic, Kanita
AU  - Loncar Turukalo, Tatjana
AU  - Przymus, Piotr
AU  - Trajkovik, Vladimir
AU  - Aasmets, Oliver
AU  - Berland, Magali
AU  - Gruca, Aleksandra
AU  - Hasic, Jasminka
AU  - Hron, Karel
AU  - Klammsteiner, Thomas
AU  - Kolev, Mikhail
AU  - Lahti, Leo
AU  - Lopes, Marta B
AU  - Moreno, Victor
AU  - Naskinova, Irina
AU  - Org, Elin
AU  - Paciência, Inês
AU  - Papoutsoglou, Georgios
AU  - Shigdel, Rajesh
AU  - Stres, Blaz
AU  - Vilne, Baiba
AU  - Yousef, Malik
AU  - Zdravevski, Eftim
AU  - Tsamardinos, Ioannis
AU  - Carrillo de Santa Pau, Enrique
AU  - Claesson, Marcus J
AU  - Moreno-Indias, Isabel
AU  - Truu, Jaak
PY  - 2021
DO  - 10.3389/fmicb.2021.634511
SN  - 1664-302X
UR  - https://hdl.handle.net/10668/26927
T2  - Frontiers in microbiology
AB  - The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the...
LA  - en
KW  - biomarker identification
KW  - disease prediction
KW  - feature selection
KW  - machine learning
KW  - microbiome
TI  - Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.
TY  - research article
VL  - 12
ER  -