Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions
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Published Version
Date
2021-02
Authors
Moreno-Indias, Isabel
Lahti, Leo
Nedyalkova, Miroslava
Elbere, Ilze
Roshchupkin, Gennady
Adilovic, Muhamed
Aydemir, Onder
Bakir-Gungor, Burcu
Pau, Enrique Carrillo De Santa
D'Elia, Domenica
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers Media S.A.
Published Version
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.
Description
Keywords
Machine learning , Microbiome , ML4Microbiome , Personalized medicine , Biomarker identification
Citation
Moreno-Indias, I., Lahti, L., Nedyalkova, M., Elbere, I., Roshchupkin, G., Adilovic, M., Aydemir, O., Bakir-Gungor, B., Pau, E. C. D. S., D'Elia, D., Desai, M., Falquet, L., Gundogdu, A., Hron, K., Klammsteiner, T., Lopes, M. B., Marcos-Zambrano, L. J., Marques, C., Mason, M., May, P., Pasic, L., Pio, G., Pongor, S., Promponas, V. J., Przymus, P., Saez-Rodriguez, J., Sampri, A., Shigdel, R., Stres, B., Suharoschi, R., Truu, J., Truica, C-O., Vilne, B., Vlachakis, D. P., Yilmaz, E., Zeller, G., Zomer, A., Gomez-Cabrero, D. and Claesson, M. J. (2021) 'Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions', Frontiers In Microbiology, 12, 635781, (9pp). doi: 10.3389/fmicb.2021.635781