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Autor
Adilovic, Muhamed 1 Aydemir, Onder 1 Bakir-Gungor, Burcu 1 Claesson, Marcus J 1 D'Elia, Domenica 1 Desai, Mahesh S 1 Elbere, Ilze 1 Falquet, Laurent 1 Gundogdu, Aycan 1 Gómez-Cabrero, David 1 Hron, Karel 1 Klammsteiner, Thomas 1 Lahti, Leo 1 Lopes, Marta B 1 Marcos-Zambrano, Laura Judith 1 Marques, Cláudia 1 Mason, Michael 1 May, Patrick 1 Moreno-Indias, Isabel 1 Nedyalkova, Miroslava 1
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Bioinformatics Core Luxembourg Centre... 1 Bioinformatics Research Laboratory De... 1 Bioinformatics Research Unit Riga Str... 1 Biotechnical Faculty University of Lj... 1 CINTESIS NOVA Medical School NMS Univ... 1 Centro de Investigación Biomeìdica en... 1 Centro de Matemática e Aplicações FCT... 1 Computational Biology Group Precision... 1 Computational Oncology Sage Bionetwor... 1 Department for Biomedical Sciences In... 1 Department of Biology University of F... 1 Department of Clinical Science Univer... 1 Department of Computer Engineering Ab... 1 Department of Computer Science Univer... 1 Department of Computer Science and En... 1 Department of Computer Technologies K... 1 Department of Computing University of... 1 Department of Electrical and Electron... 1 Department of Epidemiology Erasmus Me... 1 Department of Genetics and Bioenginee... 1
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Autor
Adilovic, Muhamed 1 Aydemir, Onder 1 Bakir-Gungor, Burcu 1 Claesson, Marcus J 1 D'Elia, Domenica 1 Desai, Mahesh S 1 Elbere, Ilze 1 Falquet, Laurent 1 Gundogdu, Aycan 1 Gómez-Cabrero, David 1 Hron, Karel 1 Klammsteiner, Thomas 1 Lahti, Leo 1 Lopes, Marta B 1 Marcos-Zambrano, Laura Judith 1 Marques, Cláudia 1 Mason, Michael 1 May, Patrick 1 Moreno-Indias, Isabel 1 Nedyalkova, Miroslava 1
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Pracoviště
Bioinformatics Core Luxembourg Centre... 1 Bioinformatics Research Laboratory De... 1 Bioinformatics Research Unit Riga Str... 1 Biotechnical Faculty University of Lj... 1 CINTESIS NOVA Medical School NMS Univ... 1 Centro de Investigación Biomeìdica en... 1 Centro de Matemática e Aplicações FCT... 1 Computational Biology Group Precision... 1 Computational Oncology Sage Bionetwor... 1 Department for Biomedical Sciences In... 1 Department of Biology University of F... 1 Department of Clinical Science Univer... 1 Department of Computer Engineering Ab... 1 Department of Computer Science Univer... 1 Department of Computer Science and En... 1 Department of Computer Technologies K... 1 Department of Computing University of... 1 Department of Electrical and Electron... 1 Department of Epidemiology Erasmus Me... 1 Department of Genetics and Bioenginee... 1
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Moreno-Indias, Isabel
Autor Moreno-Indias, Isabel Instituto de Investigación Biomédica de Málaga (IBIMA), Unidad de Gestión Clìnica de Endocrinologìa y Nutrición, Hospital Clìnico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Lahti, Leo
- Nedyalkova, Miroslava
- Elbere, Ilze
- Roshchupkin, Gennady
- Adilovic, Muhamed
- Aydemir, Onder
- Bakir-Gungor, Burcu
- Santa Pau, Enrique Carrillo-de
- D'Elia, Domenica
Free Medical Journals od 2010
PubMed Central od 2010
Europe PubMed Central od 2010
Open Access Digital Library od 2010-01-01
Open Access Digital Library od 2010-01-01
ROAD: Directory of Open Access Scholarly Resources od 2010
PubMed
33692771
DOI
10.3389/fmicb.2021.635781
Knihovny.cz E-zdroje
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.
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Po ukončení testovacího provozu bude odkaz přesměrován adresu produkční verze portálu Medvik.