Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101548977 Publication Model: eCollection Cited Medium: Print ISSN: 1664-302X (Print) Linking ISSN: 1664302X NLM ISO Abbreviation: Front Microbiol Subsets: PubMed not MEDLINE

Original Publication: Lausanne : Frontiers Research Foundation

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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Moreno-Indias, Lahti, Nedyalkova, Elbere, Roshchupkin, Adilovic, Aydemir, Bakir-Gungor, Santa Pau, D’Elia, Desai, Falquet, Gundogdu, Hron, Klammsteiner, Lopes, Marcos-Zambrano, Marques, Mason, May, Pašić, Pio, Pongor, Promponas, Przymus, Saez-Rodriguez, Sampri, Shigdel, Stres, Suharoschi, Truu, Truică, Vilne, Vlachakis, Yilmaz, Zeller, Zomer, Gómez-Cabrero and Claesson.)

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Keywords: ML4Microbiome; biomarker identification; machine learning; microbiome; personalized medicine

Date Created: 20210311 Latest Revision: 20231111

20231111

PMC7937616

10.3389/fmicb.2021.635781

33692771