Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

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 development and progression of various complex d...

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Main Authors: Laura Judith Marcos-Zambrano, Kanita Karaduzovic-Hadziabdic, Tatjana Loncar Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland, Aleksandra Gruca, Jasminka Hasic, Karel Hron, Thomas Klammsteiner, Mikhail Kolev, Leo Lahti, Marta B. Lopes, Victor Moreno, Irina Naskinova, Elin Org, Inês Paciência, Georgios Papoutsoglou, Rajesh Shigdel, Blaz Stres, Baiba Vilne, Malik Yousef, Eftim Zdravevski, Ioannis Tsamardinos, Enrique Carrillo de Santa Pau, Marcus J. Claesson, Isabel Moreno-Indias, Jaak Truu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-02-01
Series:Frontiers in Microbiology
Subjects:
microbiome
machine learning
disease prediction
biomarker identification
feature selection
Online Access:https://www.frontiersin.org/articles/10.3389/fmicb.2021.634511/full

Internet

https://www.frontiersin.org/articles/10.3389/fmicb.2021.634511/full

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