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Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions

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 lifetime. Whereas many of the challenges in microbiome research are similar to other high-throughput...

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Main Authors: Moreno-Indias, Isabel (Author) , Lahti, Leo (Author) , Nedyalkova, Miroslava (Author) , Elbere, Ilze (Author) , Roshchupkin, Gennady (Author) , Adilovic, Muhamed (Author) , Aydemir, Onder (Author) , Bakir-Gungor, Burcu (Author) , Santa Pau, Enrique Carrillo-de (Author) , D’Elia, Domenica (Author) , Desai, Mahesh S. (Author) , Falquet, Laurent (Author) , Gundogdu, Aycan (Author) , Hron, Karel (Author) , Klammsteiner, Thomas (Author) , Lopes, Marta B. (Author) , Marcos-Zambrano, Laura Judith (Author) , Marques, Cláudia (Author) , Mason, Michael (Author) , May, Patrick (Author) , Pašić, Lejla (Author) , Pio, Gianvito (Author) , Pongor, Sándor (Author) , Promponas, Vasilis J. (Author) , Przymus, Piotr (Author) , Sáez Rodríguez, Julio (Author) , Sampri, Alexia (Author) , Shigdel, Rajesh (Author) , Stres, Blaz (Author) , Suharoschi, Ramona (Author) , Truu, Jaak (Author) , Truică, Ciprian-Octavian (Author) , Vilne, Baiba (Author) , Vlachakis, Dimitrios (Author) , Yilmaz, Ercument (Author) , Zeller, Georg F. (Author) , Zomer, Aldert L. (Author) , Gómez-Cabrero, David (Author) , Claesson, Marcus J. (Author)
Format: Article (Journal)
Language:English
Published: 22 February 2021
In: Frontiers in microbiology
Year: 2021, Volume: 12, Pages: 1-9
ISSN:1664-302X
DOI:10.3389/fmicb.2021.635781
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3389/fmicb.2021.635781
Verlag, lizenzpflichtig, Volltext: https://www.frontiersin.org/articles/10.3389/fmicb.2021.635781/full
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Author Notes:Isabel Moreno-Indias, Leo Lahti, Miroslava Nedyalkova, Ilze Elbere, Gennady Roshchupkin, Muhamed Adilovic, Onder Aydemir, Burcu Bakir-Gungor, Enrique Carrillo-de Santa Pau, Domenica D’Elia, Mahesh S. Desai, Laurent Falquet, Aycan Gundogdu, Karel Hron, Thomas Klammsteiner, Marta B. Lopes, Laura Judith Marcos-Zambrano, Cláudia Marques, Michael Mason, Patrick May, Lejla Pašić, Gianvito Pio, Sándor Pongor, Vasilis J. Promponas, Piotr Przymus, Julio Saez-Rodriguez, Alexia Sampri, Rajesh Shigdel, Blaz Stres, Ramona Suharoschi, Jaak Truu, Ciprian-Octavian Truică, Baiba Vilne, Dimitrios Vlachakis, Ercument Yilmaz, Georg Zeller, Aldert L. Zomer, David Gómez-Cabrero and Marcus J. Claesson on behalf of ML4Microbiom
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Summary: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 lifetime. Whereas 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 standardisation of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
Item Description:Gesehen am 04.05.2021
Physical Description:Online Resource
ISSN:1664-302X
DOI:10.3389/fmicb.2021.635781

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