Reference : Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary...
Scientific journals : Article
Life sciences : Environmental sciences & ecology
Life sciences : Microbiology
Systems Biomedicine
http://hdl.handle.net/10993/46352
Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
English
Moreno-Indias, Isabel [> >]
Lahti, Leo [> >]
Nedyalkova, Miroslava [> >]
Elbere, Ilze [> >]
Roshchupkin, Gennady [> >]
Adilovic, Muhamed [> >]
Aydemir, Onder [> >]
Bakir-Gungor, Burcu [> >]
Santa Pau, Enrique Carrillo-De [> >]
D’Elia, Domenica [> >]
Desai, Mahesh mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) >]
Falquet, Laurent [> >]
Gundogdu, Aycan [> >]
Hron, Karel [> >]
Klammsteiner, Thomas [> >]
Lopes, Marta B. [> >]
Marcos-Zambrano, Laura Judith [> >]
Marques, Cláudia [> >]
Mason, Michael [> >]
May, Patrick mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
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. [> >]
Gómez-Cabrero, David [> >]
Claesson, Marcus J. [> >]
22-Feb-2021
Frontiers in Microbiology
12
277
Yes
International
1664-302X
[en] Microbiome ; Statistical Learning ; Machine Learning
[en] 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 of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Institute of Health - LIH
COST Action CA1813, CORE grant (C18/BM/12585940)
Researchers ; Professionals
http://hdl.handle.net/10993/46352
10.3389/fmicb.2021.635781
https://www.frontiersin.org/article/10.3389/fmicb.2021.635781

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