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Statistical and Machine Learning Techniques in Human Microb..:

Moreno-Indias, I ; Lahti, L ; Nedyalkova, M...
Moreno-Indias , I , Lahti , L , Nedyalkova , M , Elbere , I , Roshchupkin , G , Adilovic , M , Aydemir , O , Bakir-Gungor , B , Pau , ECD , D'Elia , D , Desai , MS , Falquet , L , Gundogdu , A , Hron , K , Klammsteiner , T , Lopes , MB , Marcos-Zambrano , LJ , Marques , C , Mason , M , May , P , Pasic , L , Pio , G , Pongor , S , Promponas , VJ , Przymus , P , Saez-Rodriguez , J , Sampri , A , Shigdel , R , Stres , B , Suharoschi , R , Truu , J , Truica , CO , Vilne , B , Vlachakis , D , Yilmaz , E , Zeller , G , Zomer , AL , Gomez-Cabrero , D & Claesson , MJ 2021 , ' Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions ' , Frontiers in Microbiology , vol. 12 , 635781 . https://doi.org/10.3389/fmicb.2021.635781. , 2021

Link: https://pure.eur.nl/en/publications/769a7f10-0a86-4075..

RT Journal
T1 <Title>Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions</Title>

UL https://suche.suub.uni-bremen.de/peid=base-fturottercrispub:oai:pure.eur.nl:publications_769a7f10-0a86-4075-be84-8e9a28e1dd7a&Exemplar=1&LAN=DE
				
A1 Moreno-Indias, I
A1 Lahti, L
A1 Nedyalkova, M
A1 Elbere, I
A1 Roshchupkin, Gennady
A1 Adilovic, M
A1 Aydemir, O
A1 Bakir-Gungor, B
A1 Pau, ECD
A1 D'Elia, D
A1 Desai, MS
A1 Falquet, L
A1 Gundogdu, A
A1 Hron, K
A1 Klammsteiner, T
A1 Lopes, MB
A1 Marcos-Zambrano, LJ
A1 Marques, C
A1 Mason, M
A1 May, P
A1 Pasic, L
A1 Pio, G
A1 Pongor, S
A1 Promponas, VJ
A1 Przymus, P
A1 Saez-Rodriguez, J
A1 Sampri, A
A1 Shigdel, R
A1 Stres, B
A1 Suharoschi, R
A1 Truu, J
A1 Truica, CO
A1 Vilne, B
A1 Vlachakis, D
A1 Yilmaz, E
A1 Zeller, G
A1 Zomer, AL
A1 Gomez-Cabrero, D
A1 Claesson, MJ
YR 2021
JF Moreno-Indias , I , Lahti , L , Nedyalkova , M , Elbere , I , Roshchupkin , G , Adilovic , M , Aydemir , O , Bakir-Gungor , B , Pau , ECD , D'Elia , D , Desai , MS , Falquet , L , Gundogdu , A , Hron , K , Klammsteiner , T , Lopes , MB , Marcos-Zambrano , LJ , Marques , C , Mason , M , May , P , Pasic , L , Pio , G , Pongor , S , Promponas , VJ , Przymus , P , Saez-Rodriguez , J , Sampri , A , Shigdel , R , Stres , B , Suharoschi , R , Truu , J , Truica , CO , Vilne , B , Vlachakis , D , Yilmaz , E , Zeller , G , Zomer , AL , Gomez-Cabrero , D & Claesson , MJ 2021 , ' Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions ' , Frontiers in Microbiology , vol. 12 , 635781 . https://doi.org/10.3389/fmicb.2021.635781
LK http://dx.doi.org/https://pure.eur.nl/en/publications/769a7f10-0a86-4075-be84-8e9a28e1dd7a
DO https://pure.eur.nl/en/publications/769a7f10-0a86-4075-be84-8e9a28e1dd7a
SF ELIB - SuUB Bremen

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