Article (Scientific journals)
Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava et al.
2021In Frontiers in Microbiology, 12, p. 277
Peer Reviewed verified by ORBi
 

Files


Full Text
Moreno-Indias2021.fmicb-12-635781.pdf
Publisher postprint (976.13 kB)
Request a copy

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
Microbiome; Statistical Learning; Machine Learning
Abstract :
[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.
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
LIH - Luxembourg Institute of Health
Disciplines :
Microbiology
Environmental sciences & ecology
Author, co-author :
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 ;  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  ;  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.
More authors (29 more) Less
External co-authors :
yes
Language :
English
Title :
Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
Publication date :
22 February 2021
Journal title :
Frontiers in Microbiology
ISSN :
1664-302X
Publisher :
Frontiers, Lausanne, Switzerland
Volume :
12
Pages :
277
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Systems Biomedicine
Funders :
COST Action CA1813, CORE grant (C18/BM/12585940)
Available on ORBilu :
since 22 February 2021

Statistics


Number of views
120 (4 by Unilu)
Number of downloads
5 (2 by Unilu)

Scopus citations®
 
47
Scopus citations®
without self-citations
29
OpenCitations
 
29
WoS citations
 
44

Bibliography


Similar publications



Contact ORBilu