Author:
Publisher: Elsevier
ISBN: 0444640452
Category : Science
Languages : en
Pages : 732
Book Description
Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more. - Presents the best reference book for omics data analysis - Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools - Includes examples of applications in research fields, such as environmental, biomedical and food analysis
Data Analysis for Omic Sciences: Methods and Applications
Author:
Publisher: Elsevier
ISBN: 0444640452
Category : Science
Languages : en
Pages : 732
Book Description
Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more. - Presents the best reference book for omics data analysis - Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools - Includes examples of applications in research fields, such as environmental, biomedical and food analysis
Publisher: Elsevier
ISBN: 0444640452
Category : Science
Languages : en
Pages : 732
Book Description
Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more. - Presents the best reference book for omics data analysis - Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools - Includes examples of applications in research fields, such as environmental, biomedical and food analysis
Bioinformatics and Biomarker Discovery
Author: Francisco Azuaje
Publisher: John Wiley & Sons
ISBN: 111996430X
Category : Science
Languages : en
Pages : 206
Book Description
This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations
Publisher: John Wiley & Sons
ISBN: 111996430X
Category : Science
Languages : en
Pages : 206
Book Description
This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations
Bioinformatics for Omics Data
Author: Bernd Mayer
Publisher: Springer Science+Business Media
ISBN: 9781617790270
Category : Bioinformatics
Languages : en
Pages : 584
Book Description
Presenting an area of research that intersects with and integrates diverse disciplines, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study.
Publisher: Springer Science+Business Media
ISBN: 9781617790270
Category : Bioinformatics
Languages : en
Pages : 584
Book Description
Presenting an area of research that intersects with and integrates diverse disciplines, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study.
Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
Author: Susmita Datta
Publisher: Springer
ISBN: 3319458094
Category : Medical
Languages : en
Pages : 294
Book Description
This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.
Publisher: Springer
ISBN: 3319458094
Category : Medical
Languages : en
Pages : 294
Book Description
This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.
Foodomics
Author: Jorge Barros-Velázquez
Publisher: Royal Society of Chemistry
ISBN: 1839163011
Category : Science
Languages : en
Pages : 384
Book Description
Presenting an up-to-date review of the state-of-the-art and main applications of omics technologies to current hot topics in food sciences, this book is divided into four convenient sections. The first section represents an introduction to the development of foodomics and will provide a general overview of DNA-based and protein-based methods. The second section is focused on the main applications of omics to food safety issues, such as chemical hazards, foodborne pathogens, phages, food authentication or GMO detection. The third section is focused on specific food groups and how omics have revolutionized the investigation of dairy and meat products, seafood, agricultural and fermented food products. Finally, the fourth section is devoted to the link between foodomics and health: hot topics such as nutrimetabolomics, food allergy or probiotics are reviewed here. The book brings together work from top international scientists to produce the most significant academic book for some years on omics and food for a broad audience. It presents unique features not covered so far by other books, such as a detailed description of different strategies and applications of omics techniques to many food sectors and provides a welcome addition to the cutting-edge literature in this area for researchers and professionals in food science and food chemistry.
Publisher: Royal Society of Chemistry
ISBN: 1839163011
Category : Science
Languages : en
Pages : 384
Book Description
Presenting an up-to-date review of the state-of-the-art and main applications of omics technologies to current hot topics in food sciences, this book is divided into four convenient sections. The first section represents an introduction to the development of foodomics and will provide a general overview of DNA-based and protein-based methods. The second section is focused on the main applications of omics to food safety issues, such as chemical hazards, foodborne pathogens, phages, food authentication or GMO detection. The third section is focused on specific food groups and how omics have revolutionized the investigation of dairy and meat products, seafood, agricultural and fermented food products. Finally, the fourth section is devoted to the link between foodomics and health: hot topics such as nutrimetabolomics, food allergy or probiotics are reviewed here. The book brings together work from top international scientists to produce the most significant academic book for some years on omics and food for a broad audience. It presents unique features not covered so far by other books, such as a detailed description of different strategies and applications of omics techniques to many food sectors and provides a welcome addition to the cutting-edge literature in this area for researchers and professionals in food science and food chemistry.
Data Analysis and Chemometrics for Metabolomics
Author: Richard G. Brereton
Publisher: John Wiley & Sons
ISBN: 1119639409
Category : Science
Languages : en
Pages : 437
Book Description
Understand new modes of analysing metabolomic data Metabolomics is the study of metabolites, small molecules and chemical substrates within cells or larger structures which collectively make up the metabolome. The field of metabolomics stands to benefit enormously from chemometrics, an approach which brings advanced statistical techniques to bear on data of this kind. Data Analysis and Chemometrics for Metabolomics constitutes an accessible introduction to chemometric techniques and their applications in the field of metabolomics. Thoroughly and accessibly written by a leading expert in chemometrics, and printed in full-colour, it brings robust data analysis into conversation with the metabolomic field to the immense benefit of practitioners. Data Analysis and Chemometrics for Metabolomics readers will also find: Statistical insights into the nature of metabolomic hypothesis testing, validation, and more All metabolomics data sets from the book on a companion website Case studies from human, animal, plant and bacterial biology Data Analysis and Chemometrics for Metabolomics is ideal for practitioners in the life sciences, clinical sciences and chemistry, as well as metabolomics researchers or developers of research instruments looking to apply cutting-edge analytical techniques, and statisticians developing methods to design experiments and analyse large datasets of clinical and biological origin.
Publisher: John Wiley & Sons
ISBN: 1119639409
Category : Science
Languages : en
Pages : 437
Book Description
Understand new modes of analysing metabolomic data Metabolomics is the study of metabolites, small molecules and chemical substrates within cells or larger structures which collectively make up the metabolome. The field of metabolomics stands to benefit enormously from chemometrics, an approach which brings advanced statistical techniques to bear on data of this kind. Data Analysis and Chemometrics for Metabolomics constitutes an accessible introduction to chemometric techniques and their applications in the field of metabolomics. Thoroughly and accessibly written by a leading expert in chemometrics, and printed in full-colour, it brings robust data analysis into conversation with the metabolomic field to the immense benefit of practitioners. Data Analysis and Chemometrics for Metabolomics readers will also find: Statistical insights into the nature of metabolomic hypothesis testing, validation, and more All metabolomics data sets from the book on a companion website Case studies from human, animal, plant and bacterial biology Data Analysis and Chemometrics for Metabolomics is ideal for practitioners in the life sciences, clinical sciences and chemistry, as well as metabolomics researchers or developers of research instruments looking to apply cutting-edge analytical techniques, and statisticians developing methods to design experiments and analyse large datasets of clinical and biological origin.
Integrating Omics Data
Author: George Tseng
Publisher: Cambridge University Press
ISBN: 1107069114
Category : Mathematics
Languages : en
Pages : 497
Book Description
Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.
Publisher: Cambridge University Press
ISBN: 1107069114
Category : Mathematics
Languages : en
Pages : 497
Book Description
Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.
Omic Association Studies with R and Bioconductor
Author: Juan R. González
Publisher: CRC Press
ISBN: 0429803362
Category : Mathematics
Languages : en
Pages : 356
Book Description
After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions
Publisher: CRC Press
ISBN: 0429803362
Category : Mathematics
Languages : en
Pages : 356
Book Description
After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions
Big Data in Omics and Imaging
Author: Momiao Xiong
Publisher: CRC Press
ISBN: 135117262X
Category : Mathematics
Languages : en
Pages : 580
Book Description
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Publisher: CRC Press
ISBN: 135117262X
Category : Mathematics
Languages : en
Pages : 580
Book Description
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Comprehensive Foodomics
Author:
Publisher: Elsevier
ISBN: 0128163968
Category : Science
Languages : en
Pages : 2444
Book Description
Comprehensive Foodomics, Three Volume Set offers a definitive collection of over 150 articles that provide researchers with innovative answers to crucial questions relating to food quality, safety and its vital and complex links to our health. Topics covered include transcriptomics, proteomics, metabolomics, genomics, green foodomics, epigenetics and noncoding RNA, food safety, food bioactivity and health, food quality and traceability, data treatment and systems biology. Logically structured into 10 focused sections, each article is authored by world leading scientists who cover the whole breadth of Omics and related technologies, including the latest advances and applications. By bringing all this information together in an easily navigable reference, food scientists and nutritionists in both academia and industry will find it the perfect, modern day compendium for frequent reference. List of sections and Section Editors: Genomics - Olivia McAuliffe, Dept of Food Biosciences, Moorepark, Fermoy, Co. Cork, Ireland Epigenetics & Noncoding RNA - Juan Cui, Department of Computer Science & Engineering, University of Nebraska-Lincoln, Lincoln, NE Transcriptomics - Robert Henry, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia Proteomics - Jens Brockmeyer, Institute of Biochemistry and Technical Biochemistry, University Stuttgart, Germany Metabolomics - Philippe Schmitt-Kopplin, Research Unit Analytical BioGeoChemistry, Neuherberg, Germany Omics data treatment, System Biology and Foodomics - Carlos Leon Canseco, Visiting Professor, Biomedical Engineering, Universidad Carlos III de Madrid Green Foodomics - Elena Ibanez, Foodomics Lab, CIAL, CSIC, Madrid, Spain Food safety and Foodomics - Djuro Josic, Professor Medicine (Research) Warren Alpert Medical School, Brown University, Providence, RI, USA & Sandra Kraljevic Pavelic, University of Rijeka, Department of Biotechnology, Rijeka, Croatia Food Quality, Traceability and Foodomics - Daniel Cozzolino, Centre for Nutrition and Food Sciences, The University of Queensland, Queensland, Australia Food Bioactivity, Health and Foodomics - Miguel Herrero, Department of Bioactivity and Food Analysis, Foodomics Lab, CIAL, CSIC, Madrid, Spain Brings all relevant foodomics information together in one place, offering readers a ‘one-stop,’ comprehensive resource for access to a wealth of information Includes articles written by academics and practitioners from various fields and regions Provides an ideal resource for students, researchers and professionals who need to find relevant information quickly and easily Includes content from high quality authors from across the globe
Publisher: Elsevier
ISBN: 0128163968
Category : Science
Languages : en
Pages : 2444
Book Description
Comprehensive Foodomics, Three Volume Set offers a definitive collection of over 150 articles that provide researchers with innovative answers to crucial questions relating to food quality, safety and its vital and complex links to our health. Topics covered include transcriptomics, proteomics, metabolomics, genomics, green foodomics, epigenetics and noncoding RNA, food safety, food bioactivity and health, food quality and traceability, data treatment and systems biology. Logically structured into 10 focused sections, each article is authored by world leading scientists who cover the whole breadth of Omics and related technologies, including the latest advances and applications. By bringing all this information together in an easily navigable reference, food scientists and nutritionists in both academia and industry will find it the perfect, modern day compendium for frequent reference. List of sections and Section Editors: Genomics - Olivia McAuliffe, Dept of Food Biosciences, Moorepark, Fermoy, Co. Cork, Ireland Epigenetics & Noncoding RNA - Juan Cui, Department of Computer Science & Engineering, University of Nebraska-Lincoln, Lincoln, NE Transcriptomics - Robert Henry, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia Proteomics - Jens Brockmeyer, Institute of Biochemistry and Technical Biochemistry, University Stuttgart, Germany Metabolomics - Philippe Schmitt-Kopplin, Research Unit Analytical BioGeoChemistry, Neuherberg, Germany Omics data treatment, System Biology and Foodomics - Carlos Leon Canseco, Visiting Professor, Biomedical Engineering, Universidad Carlos III de Madrid Green Foodomics - Elena Ibanez, Foodomics Lab, CIAL, CSIC, Madrid, Spain Food safety and Foodomics - Djuro Josic, Professor Medicine (Research) Warren Alpert Medical School, Brown University, Providence, RI, USA & Sandra Kraljevic Pavelic, University of Rijeka, Department of Biotechnology, Rijeka, Croatia Food Quality, Traceability and Foodomics - Daniel Cozzolino, Centre for Nutrition and Food Sciences, The University of Queensland, Queensland, Australia Food Bioactivity, Health and Foodomics - Miguel Herrero, Department of Bioactivity and Food Analysis, Foodomics Lab, CIAL, CSIC, Madrid, Spain Brings all relevant foodomics information together in one place, offering readers a ‘one-stop,’ comprehensive resource for access to a wealth of information Includes articles written by academics and practitioners from various fields and regions Provides an ideal resource for students, researchers and professionals who need to find relevant information quickly and easily Includes content from high quality authors from across the globe