-
Je něco špatně v tomto záznamu ?
Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
I. Moreno-Indias, L. Lahti, M. Nedyalkova, I. Elbere, G. Roshchupkin, M. Adilovic, O. Aydemir, B. Bakir-Gungor, EC. Santa Pau, D. D'Elia, MS. Desai, L. Falquet, A. Gundogdu, K. Hron, T. Klammsteiner, MB. Lopes, LJ. Marcos-Zambrano, C. Marques, M....
Jazyk angličtina Země Švýcarsko
Typ dokumentu časopisecké články
NLK
Directory of Open Access Journals
od 2010
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
- 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
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc21010414
- 003
- CZ-PrNML
- 005
- 20210610144802.0
- 007
- ta
- 008
- 210413s2021 sz f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.3389/fmicb.2021.635781 $2 doi
- 035 __
- $a (PubMed)33692771
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a sz
- 100 1_
- $a Moreno-Indias, Isabel $u 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 $u Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- 245 10
- $a Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions / $c I. Moreno-Indias, L. Lahti, M. Nedyalkova, I. Elbere, G. Roshchupkin, M. Adilovic, O. Aydemir, B. Bakir-Gungor, EC. Santa Pau, D. D'Elia, MS. Desai, L. Falquet, A. Gundogdu, K. Hron, T. Klammsteiner, MB. Lopes, LJ. Marcos-Zambrano, C. Marques, M. Mason, P. May, L. Pašić, G. Pio, S. Pongor, VJ. Promponas, P. Przymus, J. Saez-Rodriguez, A. Sampri, R. Shigdel, B. Stres, R. Suharoschi, J. Truu, CO. Truică, B. Vilne, D. Vlachakis, E. Yilmaz, G. Zeller, AL. Zomer, D. Gómez-Cabrero, MJ. Claesson
- 520 9_
- $a 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.
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Lahti, Leo $u Department of Computing, University of Turku, Turku, Finland
- 700 1_
- $a Nedyalkova, Miroslava $u Human Genetics and Disease Mechanisms, Latvian Biomedical Research and Study Centre, Riga, Latvia
- 700 1_
- $a Elbere, Ilze $u Latvian Biomedical Research and Study Centre, Riga, Latvia
- 700 1_
- $a Roshchupkin, Gennady $u Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
- 700 1_
- $a Adilovic, Muhamed $u Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
- 700 1_
- $a Aydemir, Onder $u Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey
- 700 1_
- $a Bakir-Gungor, Burcu $u Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
- 700 1_
- $a Santa Pau, Enrique Carrillo-de $u Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
- 700 1_
- $a D'Elia, Domenica $u Department for Biomedical Sciences, Institute for Biomedical Technologies, National Research Council, Bari, Italy
- 700 1_
- $a Desai, Mahesh S $u Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg $u Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, University of Southern Denmark, Odense, Denmark
- 700 1_
- $a Falquet, Laurent $u Department of Biology, University of Fribourg, Fribourg, Switzerland $u Swiss Institute of Bioinformatics, Lausanne, Switzerland
- 700 1_
- $a Gundogdu, Aycan $u Department of Microbiology and Clinical Microbiology, Faculty of Medicine, Erciyes University, Kayseri, Turkey $u Metagenomics Laboratory, Genome and Stem Cell Center (GenKök), Erciyes University, Kayseri, Turkey
- 700 1_
- $a Hron, Karel $u Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
- 700 1_
- $a Klammsteiner, Thomas $u Department of Microbiology, University of Innsbruck, Innsbruck, Austria
- 700 1_
- $a Lopes, Marta B $u NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal $u Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
- 700 1_
- $a Marcos-Zambrano, Laura Judith $u Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
- 700 1_
- $a Marques, Cláudia $u CINTESIS, NOVA Medical School, NMS, Universidade Nova de Lisboa, Lisbon, Portugal
- 700 1_
- $a Mason, Michael $u Computational Oncology, Sage Bionetworks, Seattle, WA, United States
- 700 1_
- $a May, Patrick $u Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- 700 1_
- $a Pašić, Lejla $u Sarajevo Medical School, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
- 700 1_
- $a Pio, Gianvito $u Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
- 700 1_
- $a Pongor, Sándor $u Faculty of Information Tehnology and Bionics, Pázmány University, Budapest, Hungary
- 700 1_
- $a Promponas, Vasilis J $u Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
- 700 1_
- $a Przymus, Piotr $u Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruñ, Poland
- 700 1_
- $a Saez-Rodriguez, Julio $u Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Heidelberg, Germany
- 700 1_
- $a Sampri, Alexia $u Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom
- 700 1_
- $a Shigdel, Rajesh $u Department of Clinical Science, University of Bergen, Bergen, Norway
- 700 1_
- $a Stres, Blaz $u Jozef Stefan Institute, Ljubljana, Slovenia $u Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia $u Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- 700 1_
- $a Suharoschi, Ramona $u Molecular Nutrition and Proteomics Lab, Faculty of the Food Science and Technology, Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
- 700 1_
- $a Truu, Jaak $u Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- 700 1_
- $a Truică, Ciprian-Octavian $u Department of Computer Science and Engineering, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
- 700 1_
- $a Vilne, Baiba $u Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
- 700 1_
- $a Vlachakis, Dimitrios $u Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
- 700 1_
- $a Yilmaz, Ercument $u Department of Computer Technologies, Karadeniz Technical University, Trabzon, Turkey
- 700 1_
- $a Zeller, Georg $u European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
- 700 1_
- $a Zomer, Aldert L $u Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
- 700 1_
- $a Gómez-Cabrero, David $u Navarrabiomed, Complejo Hospitalario de Navarra (CHN), IdiSNA, Universidad Pública de Navarra (UPNA), Pamplona, Spain
- 700 1_
- $a Claesson, Marcus J $u School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
- 773 0_
- $w MED00181714 $t Frontiers in microbiology $x 1664-302X $g Roč. 12, č. - (2021), s. 635781
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/33692771 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y - $z 0
- 990 __
- $a 20210413 $b ABA008
- 991 __
- $a 20210610144802 $b ABA008
- 999 __
- $a ind $b bmc $g 1649802 $s 1130790
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2021 $b 12 $c - $d 635781 $e 20210222 $i 1664-302X $m Frontiers in microbiology $n Front Microbiol $x MED00181714
- LZP __
- $a Pubmed-20210413
