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 S. , Falquet Laurent , Gundogdu Aycan , Hron Karel , Klammsteiner Thomas , Lopes Marta B. , Marcos-Zambrano Laura Judith , Marques Cláudia , Mason Michael , May Patrick , 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. TITLE=Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions JOURNAL=Frontiers in Microbiology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.635781 DOI=10.3389/fmicb.2021.635781 ISSN=1664-302X ABSTRACT=

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.