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Author: Jessica Galloway-Peña
Publisher: Frontiers Media SA
ISBN: 2889456536
Category :
Languages : en
Pages : 186
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Book Description
High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field.
Author: Jessica Galloway-Peña
Publisher: Frontiers Media SA
ISBN: 2889456536
Category :
Languages : en
Pages : 186
Get Book Here
Book Description
High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field.
Author: Yinglin Xia
Publisher: Springer
ISBN: 9811315345
Category : Computers
Languages : en
Pages : 518
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Book Description
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
Author: Robert G. Beiko
Publisher:
ISBN: 9781493987283
Category : Microbiology
Languages : en
Pages : 324
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Book Description
Author: Setubal
Publisher: Frontiers Media SA
ISBN: 2832506402
Category : Science
Languages : en
Pages : 223
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Book Description
Author: Michael J. Cox
Publisher: European Respiratory Society
ISBN: 1849841020
Category : Medical
Languages : en
Pages : 261
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Book Description
Studying the lung microbiome requires a specialist approach to sampling, laboratory techniques and statistical analysis. This Monograph introduces the techniques used and discusses how respiratory sampling, 16S rRNA gene sequencing, metagenomics and the application of ecological theory can be used to examine the respiratory microbiome. It examines the different components of the respiratory microbiome: viruses and fungi in addition to the more frequently studied bacteria. It also considers a range of contexts from the paediatric microbiome and how this develops to disease of all ages including asthma and chronic obstructive pulmonary disease, chronic suppurative lung diseases, interstitial lung diseases, acquired pneumonias, transplantation, cancer and HIV, and the interaction of the respiratory microbiome and the environment.
Author: Joel Faintuch
Publisher: Academic Press
ISBN: 0128152508
Category : Science
Languages : en
Pages : 506
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Book Description
Microbiome and Metabolome in Diagnosis, Therapy, and Other Strategic Applications is the first book to simultaneously cover the microbiome and the metabolome in relevant clinical conditions. In a pioneering fashion, it addresses not only the classic intestinal environment, but also the oral, gastric, lung, skin and vaginal microbiome that is in line with the latest investigations. Nonbacterial microbiomes, such as fungi and viruses are not overlooked, and the plasma microbiome is also discussed. As plasma, brain, placenta, tumor cells, and other sterile fluids and tissues, are increasingly recognized to potentially host a microbiome, albeit a limited one, this is a timely resource. The book's editors were fortunate to have the input of renowned collaborators from nearly all continents. This is truly an international effort that brings the latest in the field to students and professionals alike. - Provides comprehensive coverage on diagnosis, therapy, pharmacotherapy and disease prevention in context of the microbiome and metabolome - Focuses on the proposed physiological or pathological conditions - Presents an up-to-date, useful reference
Author: Sean O’Donoghue
Publisher: Frontiers Media SA
ISBN: 2832506372
Category : Science
Languages : en
Pages : 159
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Book Description
Author: Ryan Oliver Marino Rego
Publisher: Frontiers Media SA
ISBN: 2889662438
Category : Science
Languages : en
Pages : 145
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Book Description
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Author: Kang Ning
Publisher: Springer Nature
ISBN: 9811982104
Category : Medical
Languages : en
Pages : 173
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Book Description
This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.
Author: Erik Bongcam-Rudloff
Publisher: Frontiers Media SA
ISBN: 2832556035
Category : Science
Languages : en
Pages : 209
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Book Description
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
