Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
33692771
PubMed Central
PMC7937616
DOI
10.3389/fmicb.2021.635781
Knihovny.cz E-zdroje
- Klíčová slova
- ML4Microbiome, biomarker identification, machine learning, microbiome, personalized medicine,
- Publikační typ
- časopisecké články MeSH
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.
Bioinformatics Research Unit Riga Stradins University Riga Latvia
Biotechnical Faculty University of Ljubljana Ljubljana Slovenia
Centro de Matemática e Aplicações FCT UNL Caparica Portugal
CINTESIS NOVA Medical School NMS Universidade Nova de Lisboa Lisbon Portugal
Computational Oncology Sage Bionetworks Seattle WA United States
Department of Biology University of Fribourg Fribourg Switzerland
Department of Clinical Science University of Bergen Bergen Norway
Department of Computer Engineering Abdullah Gul University Kayseri Turkey
Department of Computer Science University of Bari Aldo Moro Bari Italy
Department of Computer Technologies Karadeniz Technical University Trabzon Turkey
Department of Computing University of Turku Turku Finland
Department of Electrical and Electronics Engineering Karadeniz Technical University Trabzon Turkey
Department of Epidemiology Erasmus Medical Center Rotterdam Netherlands
Department of Infection and Immunity Luxembourg Institute of Health Esch sur Alzette Luxembourg
Department of Microbiology University of Innsbruck Innsbruck Austria
European Molecular Biology Laboratory Structural and Computational Biology Unit Heidelberg Germany
Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia
Faculty of Information Tehnology and Bionics Pázmány University Budapest Hungary
Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruñ Poland
Human Genetics and Disease Mechanisms Latvian Biomedical Research and Study Centre Riga Latvia
Institute of Molecular and Cell Biology University of Tartu Tartu Estonia
Jozef Stefan Institute Ljubljana Slovenia
Latvian Biomedical Research and Study Centre Riga Latvia
Metagenomics Laboratory Genome and Stem Cell Center Erciyes University Kayseri Turkey
Navarrabiomed Complejo Hospitalario de Navarra Pamplona Spain
NOVA Laboratory for Computer Science and Informatics FCT UNL Caparica Portugal
School of Microbiology and APC Microbiome Ireland University College Cork Cork Ireland
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Overview of data preprocessing for machine learning applications in human microbiome research
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action