Kooperativer Bibliotheksverbund

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Format: 1 online resource (573 p.)

Edition: 1st ed.

ISBN: 1-280-74724-2 , 9786610747245 , 0-08-046800-4

Content: Molecular biology operates at three levels - genes, proteins and metabolites. This book is unique in that it provides a comprehensive description of an approach (metabonomics) to characterise the endogenous metabolites in a living system, complementing gene and protein studies (genomics and proteomics). These ""omics"" methods form the basis for understanding biology at a systems level. The Handbook of Metabonomics and Metabolomics aims to be the definitive work on the rapidly expanding subjects of metabolic profiling, metabolite and biomarker identification, encompassing the fields

Note: Includes bibliographical references and index. , Front Cover; The Handbook of Metabonomics and Metabolomics; Copyright Page; Table of Contents; Foreword; Preface; Chapter 1 Metabonomics and Metabolomics Techniques and Their Applications in Mammalian Systems; 1.1. Metabonomics and metabolomics in relation to other "omics" approaches; 1.2. The concept of global systems biology; 1.3. Interactions between host and other genomes; 1.4. Time-scales of "-omics" events; 1.5. Samples for metabonomics and metabolomics; 1.6. Analytical technologies; 1.7. Chemometric methods; 1.8. New approaches to biomarker identification using chemometrics , 1.9. Standardisation of metabolic experiments and their reporting1.10. Overview of applications of metabonomics and metabolomics to mammalian systems; 1.11. Concluding remarks; References; Chapter 2 Cellular Metabolomics: The Quest for Pathway Structure; 2.1. Introduction; 2.2. How large is the metabolome?; 2.3. Metabolite identification; 2.4. Pathway identification; 2.5. "Omics" data integration; 2.6. Metabolic fluxes; 2.7. Metabolic networks; Acknowledgments; References; Chapter 3 NMR Spectroscopy Techniques for Application to Metabonomics; 3.1. Introduction; 3.2. Principles of NMR , 3.3. Hardware requirements and automation3.4. Sample handling; 3.5. NMR Experiments and their processing; 3.6. Data pre-processing; 3.7. Outlook; References; Chapter 4 High-Resolution Magic Angle Spinning NMR Spectroscopy; 4.1. Introduction; 4.2. Nuclear spin Hamiltonian; 4.3. Coherence averaging by magic angle spinning; 4.4. MAS NMR experiments; 4.5. Pulse sequences; 4.6. Applications; Acknowledgements; References; Chapter 5 Chromatographic and Electrophoretic Separations Combined with Mass Spectrometry for Metabonomics; Abstract; 5.1. Introduction; 5.2. Gas Chromatography-Mass Spectrometry , 5.3. Liquid Chromatography-Mass Spectrometry5.4. HPLC-MS for metabonomics; 5.5. Capillary LC-MS; 5.6. Ultra performance Liquid Chromatography-Mass Spectrometry; 5.7. Capillary zone electrophoresis-Mass Spectrometry; 5.8. GC-, LC- and CE-MS for metabonomics: A perspective; 5.9. Conclusions; References; Chapter 6 Chemometrics Techniques for Metabonomics; 6.1. Introduction; 6.2. Chemometric approaches to metabonomic studies; 6.3. Summary; 6.4. Extensions and future outlook; References; Chapter 7 Non-linear Methods for the Analysis of Metabolic Profiles; 7.1. Introduction , 7.2. Unsupervised methods7.3. Supervised methods; 7.4. Other methods; 7.5. Conclusions; Acknowledgements; References; Chapter 8 Databases and Standardisation of Reporting Methods for Metabolic Studies; 8.1. Introduction: The challenges; 8.2. Tackling the challenges; 8.3. Standards in action - a working example; 8.4. Final remarks; References; Chapter 9 Metabonomics in Preclinical Pharmaceutical Discovery and Development; 9.1. Introduction; 9.2. Background; 9.3. Methods; 9.4. Preclinical efficacy; 9.5. Preclinical toxicity; 9.6. Conclusions; References , Chapter 10 Applications of Metabonomics in Clinical Pharmaceutical R&D , English

Additional Edition: ISBN 0-444-52841-5

Language: English

UID:

Format: 1 online resource (622 pages)

ISBN: 0-12-812294-3 , 0-12-812293-5

Note: Front Cover -- The Handbook of Metabolic Phenotyping -- Copyright -- Contents -- Contributors -- Foreword -- Preface -- Chapter 1: An Overview of Metabolic Phenotyping and Its Role in Systems Biology -- 1. The History and Evolution of Metabolic Profiling -- 1.1. The Early Years -- 1.2. Modern Metabolic Profiling Using NMR Spectroscopy -- 1.3. Modern Metabolic Profiling Using Mass Spectrometry -- 1.4. Definition of the Field and Efforts in Standardization -- 1.5. Development of Computational Technologies -- 1.5.1. Multivariate Statistics -- 1.5.2. Statistical Spectroscopy -- 1.5.3. Databases -- 2. Application Areas -- 2.1. Drug Metabolism and Toxicology -- 2.2. Plant-Based Metabolomics -- 2.3. Human Disease -- 2.3.1. Hepatology and Gastroenterology -- 2.3.2. Neuropathologies -- 2.3.3. Cancer -- 2.3.4. Cardiometabolic Disease -- 2.3.5. Organ Transplantation -- 2.3.6. Renal Diseases -- 2.3.7. Infectious Diseases -- 2.3.8. Epidemiology & MWAS -- 2.3.9. Nutrition -- 2.4. Mechanistic Studies of Disease Models -- 2.4.1. In Vivo Models -- 2.4.2. In Vitro Models -- 2.5. Predictive Metabolic Phenotyping (Pharmacometabonomics) -- 2.6. Emerging Metabolic Profiling Technologies -- 3. Concluding Remarks -- References -- Chapter 2: NMR Spectroscopy Methods in Metabolic Phenotyping -- 1. The Nature of NMR Spectroscopy -- 2. NMR and the Complex Mixture Problem -- 3. NMR Sample Storage and Preparation -- 4. Initial 1H NMR Analysis, High-Throughput Screening, and Basic Editing Techniques -- 4.1. Solvent Signal Suppression -- 4.2. Editing of Basic 1D 1H NMR Spectra -- 5. Higher Dimensional NMR: The Quest for Resolution, Correlation, and Component Identity -- 5.1. ``Go-to´´ Techniques: Basic 2D NMR Methods -- 5.1.1. Heteronuclear Single Quantum Coherence -- 5.1.2. HSQC-TOCSY -- 5.1.3. TOCSY -- 5.1.4. J-Resolved. , 5.2. Diffusion-Ordered Methods for Self-Diffusion Coefficient Measurement -- 5.2.1. 2D Correlations With Diffusion Encoding by 3D NMR -- 5.2.2. Pureshift and Diffusion Encoding: A Resolution Revolution -- 6. Improving 2D Resolution Without Cost: Experimental Methods and Data Treatments -- 6.1. PSYCHE-Based 2D Correlations With Covariance Processing -- 6.2. Pure Shift 2D Heteronuclear Correlations -- 6.3. Nonuniform Sampling -- 7. Substantially Boosting Signal Sensitivity -- 7.1. Dissolution Dynamic Nuclear Polarization -- 7.2. Signal Amplification by Reversible Exchange -- 8. Quantitation -- 8.1. HSQC0 -- 9. Preparation of Data for Chemometric Analysis -- 10. Conclusion, Perspective, and Outlook -- References -- Chapter 3: The Role of Ultra Performance Liquid Chromatography-Mass Spectrometry in Metabolic Phenotyping -- 1. Introduction -- 2. The Emergence and Requirements of Ultra Performance Liquid Chromatography for Metabolic Phenotyping -- 3. The Requirements of Mass Spectrometers for Metabolic Phenotyping -- 4. Types of Data Acquired in UPLC-MS Studies -- 5. Quality Assurance and Quality Control in UPLC-MS-Based Metabolic Phenotyping -- 5.1. System Suitability Test Solution [10, 35] -- 5.2. Isotopically Labeled Internal Standards [10, 17, 80] -- 5.3. Within-Study Pooled Quality Control Sample [10, 17, 82] -- 5.4. Long-Term Reference Material [17, 85] -- 5.5. Extraction Blank Samples -- 6. Analytical Robustness for Small-Scale and Large-Scale Studies -- 7. Data Processing -- 8. Metabolite Annotation and Identification -- 9. Exemplar Applications -- Acknowledgments -- References -- Chapter 4: GC-MS-Based Metabolic Phenotyping -- 1. Introduction -- 2. GC-MS as an Analytical Technique -- 3. GC-MS Metabolic Phenotyping Workflow -- 4. Sample Preparation for GC-MS-Based Metabolic Phenotyping -- 5. Data Processing -- 6. Statistical Evaluation. , 7. Biochemical Interpretation -- 8. GC-MS Troubleshooting -- 9. Clinical Applications -- 10. Conclusion -- References -- Chapter 5: Metabolic Phenotyping Using Capillary Electrophoresis Mass Spectrometry -- 1. Introduction -- 1.1. Instrumentation -- 1.2. Principles of Capillary Zone Electrophoresis -- 1.3. Conditions That Can Be Modified to Optimize the Separation and Limitations When Coupled to MS -- 1.4. Strengths -- 1.5. Weaknesses -- 1.6. Coupling to MS -- 2. Workflow -- 2.1. Design -- 2.2. Sample -- 2.3. Separation and Detection -- 2.3.1. Materials -- 2.3.2. Capillary Preparation -- 2.3.3. CE-MS Method -- 2.4. Data -- 2.5. Statistics -- 2.6. Identification -- 2.7. Meaning -- 3. Applications -- 3.1. Cancer -- 3.2. Metabolic Disorders -- 3.3. Neurodegenerative Diseases and Brain Disorders -- 3.4. Others -- 3.4.1. Infectious Diseases -- 3.4.2. Alcohol Abuse -- 3.4.3. Clinical Approaches -- 4. Conclusions and Future Trends -- References -- Chapter 6: Supercritical Fluid Chromatography for Metabolic Phenotyping: Potential and Applications -- 1. Introduction -- 2. Application of SFC to Nonpolar Metabolite Analysis -- 2.1. Lipids -- 2.2. Bile and Bile Acids -- 2.3. Applications of SFC to Polar Metabolite Analysis -- 3. Conclusion -- References -- Chapter 7: The iKnife: Development and Clinical Applications of Rapid Evaporative Ionization Mass Spectrometry -- Abbreviations -- 1. Introduction -- 2. Basic Principles of Mass Spectrometry -- 3. Nonambient and Ambient Mass Spectrometry -- 4. Development of Rapid Evaporative Ionization Mass Spectrometry (REIMS) (the iKnife) -- 5. Model Generation: The Concept of Profiling -- 6. Clinical Applications -- 6.1. Oncological Surgery: Introduction -- 6.2. Breast Cancer: The iKnife as a Diagnostic and Margin Assessment Tool -- 6.3. Brain Tumors: The iKnife as a Diagnostic and Extent of Resection Assessment Tool. , 6.4. Gastroenterology: The iEndoscope -- 7. Microbiology Applications -- 8. Conclusions -- References -- Chapter 8: Univariate Statistical Modeling, Multiple Testing Correction, and Visualization in Metabolome-Wide Association ... -- 1. Metabolic Phenotyping: Opportunities and Challenges -- 1.1. Application to Public Health Studies -- 1.2. Overview of the Analytical Pipeline in Metabolic Phenotyping -- 2. Metabolome-Wide Association Study: Univariate Approaches -- 2.1. Imputation and Normalization -- 2.2. Statistical Analyses Using Univariate Approaches -- 2.3. Linear and Generalized Linear Models -- 2.4. (Generalized) Linear Mixed Models -- 2.5. Meta-Analyses -- 3. Metabolome-Wide Significance Level -- 3.1. FWER and FDR Control -- 3.2. Defining the Metabolome-Wide Significance Level -- 4. Results Prioritization and Visualization -- 5. Conclusions -- Acknowledgment -- References -- Chapter 9: Multivariate Statistical Methods for Metabolic Phenotyping -- 1. Introduction -- 1.1. Mathematical Notations -- 1.2. Data Transformations -- 1.2.1. Mean Centering -- 1.2.2. Autoscaling -- 1.2.3. Generalized Equation for Scaling -- 1.3. Data Set Partitioning -- 2. Unsupervised Pattern Recognition -- 2.1. Unsupervised Methods for Dimension Reduction and Multivariate Projections -- 2.1.1. Principal Component Analysis -- 2.1.2. Projection Pursuit and Independent Component Analysis -- 2.1.3. Kernel Principal Component Analysis -- 2.1.4. Sparse Principal Component Analysis -- 2.2. Outlier Detection -- 2.2.1. Hotelling's T2 -- 2.2.2. Quality Control Samples -- 2.2.3. Robust Principal Component Analysis -- 2.3. Other Data Exploration Approaches for Specific Experimental Designs and Multiway Analysis -- 2.3.1. Multiway Methods -- 2.3.2. Multivariate Curve Resolution -- 2.3.3. Nonnegative Principal Component Analysis and Nonnegative Matrix Factorization. , 3. Supervised Pattern Recognition and Multivariate Regression -- 3.1. Discriminant and Cluster Analysis -- 3.1.1. Linear Discriminant Analysis -- 3.1.2. Quadratic Discriminant Analysis -- 3.1.3. k-Nearest Neighbors -- 3.1.4. k-Means, k-Medoids, and Fuzzy Alternatives -- 3.2. Multivariate Regression -- 3.2.1. Multiple Linear Regression -- 3.2.2. Principal Component Regression -- 3.2.3. Partial Least Squares -- 3.2.4. Kernel Partial Least Squares -- 3.2.5. Orthogonal Signal Correction-Filtered Partial Least Squares and Orthogonal Projections to Latent Structures -- 3.2.6. Two-Block Orthogonal Projections to Latent Structures -- 3.2.7. Kernel Orthogonal Projections to Latent Structures -- 3.3. Penalized Regression -- 3.3.1. Ridge Regression and Kernel Ridge Regression -- 3.3.2. Sparse Penalized Regression Using the Lasso -- 3.3.3. The Elastic Net, a Weighted Average of Lasso and Ridge Regression -- 3.4. Nonparametric Machine Learning and Recursive Partitioning Methods -- 3.4.1. Support Vector Machines -- 3.4.2. Variable Importance in Linear Support Vector Machines -- 3.4.3. Uncovering Variable Importance for Nonlinear Kernel-Based Models -- 3.4.4. Decision Trees for Classification -- 3.4.5. Random Forests -- 3.5. Prediction Error Rates and Model Fits -- 3.6. Other Supervised Methods and Those for Specific Experimental Designs -- 3.6.1. ANOVA-Based Decompositions -- 3.6.2. Bayesian Approaches -- 3.6.3. Deep Learning Algorithms -- 4. Statistical Approaches for Biomarker Identification -- 4.1. Statistical Total Correlation Spectroscopy -- 4.2. Correlation Networks of Biological Coregulation -- 4.3. Subset Optimization by Reference Matching and the Statistical Use of Second Dimension Data -- 4.4. Statistical Hetero Spectroscopy and Data Fusion -- 5. Conclusion -- References -- Chapter 10: Data-Driven Visualizations in Metabolic Phenotyping. , 1. Visualizing Metabolic Phenotyping Data and Analysis.

Language: English

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Format: xxii, 597 Seiten : , Illustrationen, Diagramme.

ISBN: 978-0-12-812293-8

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-0-12-812294-5

Language: English

Subjects: Biology

UID:

Format: 1 online resource (622 pages)

ISBN: 0-12-812294-3 , 0-12-812293-5

Note: Front Cover -- The Handbook of Metabolic Phenotyping -- Copyright -- Contents -- Contributors -- Foreword -- Preface -- Chapter 1: An Overview of Metabolic Phenotyping and Its Role in Systems Biology -- 1. The History and Evolution of Metabolic Profiling -- 1.1. The Early Years -- 1.2. Modern Metabolic Profiling Using NMR Spectroscopy -- 1.3. Modern Metabolic Profiling Using Mass Spectrometry -- 1.4. Definition of the Field and Efforts in Standardization -- 1.5. Development of Computational Technologies -- 1.5.1. Multivariate Statistics -- 1.5.2. Statistical Spectroscopy -- 1.5.3. Databases -- 2. Application Areas -- 2.1. Drug Metabolism and Toxicology -- 2.2. Plant-Based Metabolomics -- 2.3. Human Disease -- 2.3.1. Hepatology and Gastroenterology -- 2.3.2. Neuropathologies -- 2.3.3. Cancer -- 2.3.4. Cardiometabolic Disease -- 2.3.5. Organ Transplantation -- 2.3.6. Renal Diseases -- 2.3.7. Infectious Diseases -- 2.3.8. Epidemiology & MWAS -- 2.3.9. Nutrition -- 2.4. Mechanistic Studies of Disease Models -- 2.4.1. In Vivo Models -- 2.4.2. In Vitro Models -- 2.5. Predictive Metabolic Phenotyping (Pharmacometabonomics) -- 2.6. Emerging Metabolic Profiling Technologies -- 3. Concluding Remarks -- References -- Chapter 2: NMR Spectroscopy Methods in Metabolic Phenotyping -- 1. The Nature of NMR Spectroscopy -- 2. NMR and the Complex Mixture Problem -- 3. NMR Sample Storage and Preparation -- 4. Initial 1H NMR Analysis, High-Throughput Screening, and Basic Editing Techniques -- 4.1. Solvent Signal Suppression -- 4.2. Editing of Basic 1D 1H NMR Spectra -- 5. Higher Dimensional NMR: The Quest for Resolution, Correlation, and Component Identity -- 5.1. ``Go-to´´ Techniques: Basic 2D NMR Methods -- 5.1.1. Heteronuclear Single Quantum Coherence -- 5.1.2. HSQC-TOCSY -- 5.1.3. TOCSY -- 5.1.4. J-Resolved. , 5.2. Diffusion-Ordered Methods for Self-Diffusion Coefficient Measurement -- 5.2.1. 2D Correlations With Diffusion Encoding by 3D NMR -- 5.2.2. Pureshift and Diffusion Encoding: A Resolution Revolution -- 6. Improving 2D Resolution Without Cost: Experimental Methods and Data Treatments -- 6.1. PSYCHE-Based 2D Correlations With Covariance Processing -- 6.2. Pure Shift 2D Heteronuclear Correlations -- 6.3. Nonuniform Sampling -- 7. Substantially Boosting Signal Sensitivity -- 7.1. Dissolution Dynamic Nuclear Polarization -- 7.2. Signal Amplification by Reversible Exchange -- 8. Quantitation -- 8.1. HSQC0 -- 9. Preparation of Data for Chemometric Analysis -- 10. Conclusion, Perspective, and Outlook -- References -- Chapter 3: The Role of Ultra Performance Liquid Chromatography-Mass Spectrometry in Metabolic Phenotyping -- 1. Introduction -- 2. The Emergence and Requirements of Ultra Performance Liquid Chromatography for Metabolic Phenotyping -- 3. The Requirements of Mass Spectrometers for Metabolic Phenotyping -- 4. Types of Data Acquired in UPLC-MS Studies -- 5. Quality Assurance and Quality Control in UPLC-MS-Based Metabolic Phenotyping -- 5.1. System Suitability Test Solution [10, 35] -- 5.2. Isotopically Labeled Internal Standards [10, 17, 80] -- 5.3. Within-Study Pooled Quality Control Sample [10, 17, 82] -- 5.4. Long-Term Reference Material [17, 85] -- 5.5. Extraction Blank Samples -- 6. Analytical Robustness for Small-Scale and Large-Scale Studies -- 7. Data Processing -- 8. Metabolite Annotation and Identification -- 9. Exemplar Applications -- Acknowledgments -- References -- Chapter 4: GC-MS-Based Metabolic Phenotyping -- 1. Introduction -- 2. GC-MS as an Analytical Technique -- 3. GC-MS Metabolic Phenotyping Workflow -- 4. Sample Preparation for GC-MS-Based Metabolic Phenotyping -- 5. Data Processing -- 6. Statistical Evaluation. , 7. Biochemical Interpretation -- 8. GC-MS Troubleshooting -- 9. Clinical Applications -- 10. Conclusion -- References -- Chapter 5: Metabolic Phenotyping Using Capillary Electrophoresis Mass Spectrometry -- 1. Introduction -- 1.1. Instrumentation -- 1.2. Principles of Capillary Zone Electrophoresis -- 1.3. Conditions That Can Be Modified to Optimize the Separation and Limitations When Coupled to MS -- 1.4. Strengths -- 1.5. Weaknesses -- 1.6. Coupling to MS -- 2. Workflow -- 2.1. Design -- 2.2. Sample -- 2.3. Separation and Detection -- 2.3.1. Materials -- 2.3.2. Capillary Preparation -- 2.3.3. CE-MS Method -- 2.4. Data -- 2.5. Statistics -- 2.6. Identification -- 2.7. Meaning -- 3. Applications -- 3.1. Cancer -- 3.2. Metabolic Disorders -- 3.3. Neurodegenerative Diseases and Brain Disorders -- 3.4. Others -- 3.4.1. Infectious Diseases -- 3.4.2. Alcohol Abuse -- 3.4.3. Clinical Approaches -- 4. Conclusions and Future Trends -- References -- Chapter 6: Supercritical Fluid Chromatography for Metabolic Phenotyping: Potential and Applications -- 1. Introduction -- 2. Application of SFC to Nonpolar Metabolite Analysis -- 2.1. Lipids -- 2.2. Bile and Bile Acids -- 2.3. Applications of SFC to Polar Metabolite Analysis -- 3. Conclusion -- References -- Chapter 7: The iKnife: Development and Clinical Applications of Rapid Evaporative Ionization Mass Spectrometry -- Abbreviations -- 1. Introduction -- 2. Basic Principles of Mass Spectrometry -- 3. Nonambient and Ambient Mass Spectrometry -- 4. Development of Rapid Evaporative Ionization Mass Spectrometry (REIMS) (the iKnife) -- 5. Model Generation: The Concept of Profiling -- 6. Clinical Applications -- 6.1. Oncological Surgery: Introduction -- 6.2. Breast Cancer: The iKnife as a Diagnostic and Margin Assessment Tool -- 6.3. Brain Tumors: The iKnife as a Diagnostic and Extent of Resection Assessment Tool. , 6.4. Gastroenterology: The iEndoscope -- 7. Microbiology Applications -- 8. Conclusions -- References -- Chapter 8: Univariate Statistical Modeling, Multiple Testing Correction, and Visualization in Metabolome-Wide Association ... -- 1. Metabolic Phenotyping: Opportunities and Challenges -- 1.1. Application to Public Health Studies -- 1.2. Overview of the Analytical Pipeline in Metabolic Phenotyping -- 2. Metabolome-Wide Association Study: Univariate Approaches -- 2.1. Imputation and Normalization -- 2.2. Statistical Analyses Using Univariate Approaches -- 2.3. Linear and Generalized Linear Models -- 2.4. (Generalized) Linear Mixed Models -- 2.5. Meta-Analyses -- 3. Metabolome-Wide Significance Level -- 3.1. FWER and FDR Control -- 3.2. Defining the Metabolome-Wide Significance Level -- 4. Results Prioritization and Visualization -- 5. Conclusions -- Acknowledgment -- References -- Chapter 9: Multivariate Statistical Methods for Metabolic Phenotyping -- 1. Introduction -- 1.1. Mathematical Notations -- 1.2. Data Transformations -- 1.2.1. Mean Centering -- 1.2.2. Autoscaling -- 1.2.3. Generalized Equation for Scaling -- 1.3. Data Set Partitioning -- 2. Unsupervised Pattern Recognition -- 2.1. Unsupervised Methods for Dimension Reduction and Multivariate Projections -- 2.1.1. Principal Component Analysis -- 2.1.2. Projection Pursuit and Independent Component Analysis -- 2.1.3. Kernel Principal Component Analysis -- 2.1.4. Sparse Principal Component Analysis -- 2.2. Outlier Detection -- 2.2.1. Hotelling's T2 -- 2.2.2. Quality Control Samples -- 2.2.3. Robust Principal Component Analysis -- 2.3. Other Data Exploration Approaches for Specific Experimental Designs and Multiway Analysis -- 2.3.1. Multiway Methods -- 2.3.2. Multivariate Curve Resolution -- 2.3.3. Nonnegative Principal Component Analysis and Nonnegative Matrix Factorization. , 3. Supervised Pattern Recognition and Multivariate Regression -- 3.1. Discriminant and Cluster Analysis -- 3.1.1. Linear Discriminant Analysis -- 3.1.2. Quadratic Discriminant Analysis -- 3.1.3. k-Nearest Neighbors -- 3.1.4. k-Means, k-Medoids, and Fuzzy Alternatives -- 3.2. Multivariate Regression -- 3.2.1. Multiple Linear Regression -- 3.2.2. Principal Component Regression -- 3.2.3. Partial Least Squares -- 3.2.4. Kernel Partial Least Squares -- 3.2.5. Orthogonal Signal Correction-Filtered Partial Least Squares and Orthogonal Projections to Latent Structures -- 3.2.6. Two-Block Orthogonal Projections to Latent Structures -- 3.2.7. Kernel Orthogonal Projections to Latent Structures -- 3.3. Penalized Regression -- 3.3.1. Ridge Regression and Kernel Ridge Regression -- 3.3.2. Sparse Penalized Regression Using the Lasso -- 3.3.3. The Elastic Net, a Weighted Average of Lasso and Ridge Regression -- 3.4. Nonparametric Machine Learning and Recursive Partitioning Methods -- 3.4.1. Support Vector Machines -- 3.4.2. Variable Importance in Linear Support Vector Machines -- 3.4.3. Uncovering Variable Importance for Nonlinear Kernel-Based Models -- 3.4.4. Decision Trees for Classification -- 3.4.5. Random Forests -- 3.5. Prediction Error Rates and Model Fits -- 3.6. Other Supervised Methods and Those for Specific Experimental Designs -- 3.6.1. ANOVA-Based Decompositions -- 3.6.2. Bayesian Approaches -- 3.6.3. Deep Learning Algorithms -- 4. Statistical Approaches for Biomarker Identification -- 4.1. Statistical Total Correlation Spectroscopy -- 4.2. Correlation Networks of Biological Coregulation -- 4.3. Subset Optimization by Reference Matching and the Statistical Use of Second Dimension Data -- 4.4. Statistical Hetero Spectroscopy and Data Fusion -- 5. Conclusion -- References -- Chapter 10: Data-Driven Visualizations in Metabolic Phenotyping. , 1. Visualizing Metabolic Phenotyping Data and Analysis.

Language: English

UID:

Format: 1 online resource (622 pages)

ISBN: 0-12-812294-3 , 0-12-812293-5

Note: Front Cover -- The Handbook of Metabolic Phenotyping -- Copyright -- Contents -- Contributors -- Foreword -- Preface -- Chapter 1: An Overview of Metabolic Phenotyping and Its Role in Systems Biology -- 1. The History and Evolution of Metabolic Profiling -- 1.1. The Early Years -- 1.2. Modern Metabolic Profiling Using NMR Spectroscopy -- 1.3. Modern Metabolic Profiling Using Mass Spectrometry -- 1.4. Definition of the Field and Efforts in Standardization -- 1.5. Development of Computational Technologies -- 1.5.1. Multivariate Statistics -- 1.5.2. Statistical Spectroscopy -- 1.5.3. Databases -- 2. Application Areas -- 2.1. Drug Metabolism and Toxicology -- 2.2. Plant-Based Metabolomics -- 2.3. Human Disease -- 2.3.1. Hepatology and Gastroenterology -- 2.3.2. Neuropathologies -- 2.3.3. Cancer -- 2.3.4. Cardiometabolic Disease -- 2.3.5. Organ Transplantation -- 2.3.6. Renal Diseases -- 2.3.7. Infectious Diseases -- 2.3.8. Epidemiology & MWAS -- 2.3.9. Nutrition -- 2.4. Mechanistic Studies of Disease Models -- 2.4.1. In Vivo Models -- 2.4.2. In Vitro Models -- 2.5. Predictive Metabolic Phenotyping (Pharmacometabonomics) -- 2.6. Emerging Metabolic Profiling Technologies -- 3. Concluding Remarks -- References -- Chapter 2: NMR Spectroscopy Methods in Metabolic Phenotyping -- 1. The Nature of NMR Spectroscopy -- 2. NMR and the Complex Mixture Problem -- 3. NMR Sample Storage and Preparation -- 4. Initial 1H NMR Analysis, High-Throughput Screening, and Basic Editing Techniques -- 4.1. Solvent Signal Suppression -- 4.2. Editing of Basic 1D 1H NMR Spectra -- 5. Higher Dimensional NMR: The Quest for Resolution, Correlation, and Component Identity -- 5.1. ``Go-to´´ Techniques: Basic 2D NMR Methods -- 5.1.1. Heteronuclear Single Quantum Coherence -- 5.1.2. HSQC-TOCSY -- 5.1.3. TOCSY -- 5.1.4. J-Resolved. , 5.2. Diffusion-Ordered Methods for Self-Diffusion Coefficient Measurement -- 5.2.1. 2D Correlations With Diffusion Encoding by 3D NMR -- 5.2.2. Pureshift and Diffusion Encoding: A Resolution Revolution -- 6. Improving 2D Resolution Without Cost: Experimental Methods and Data Treatments -- 6.1. PSYCHE-Based 2D Correlations With Covariance Processing -- 6.2. Pure Shift 2D Heteronuclear Correlations -- 6.3. Nonuniform Sampling -- 7. Substantially Boosting Signal Sensitivity -- 7.1. Dissolution Dynamic Nuclear Polarization -- 7.2. Signal Amplification by Reversible Exchange -- 8. Quantitation -- 8.1. HSQC0 -- 9. Preparation of Data for Chemometric Analysis -- 10. Conclusion, Perspective, and Outlook -- References -- Chapter 3: The Role of Ultra Performance Liquid Chromatography-Mass Spectrometry in Metabolic Phenotyping -- 1. Introduction -- 2. The Emergence and Requirements of Ultra Performance Liquid Chromatography for Metabolic Phenotyping -- 3. The Requirements of Mass Spectrometers for Metabolic Phenotyping -- 4. Types of Data Acquired in UPLC-MS Studies -- 5. Quality Assurance and Quality Control in UPLC-MS-Based Metabolic Phenotyping -- 5.1. System Suitability Test Solution [10, 35] -- 5.2. Isotopically Labeled Internal Standards [10, 17, 80] -- 5.3. Within-Study Pooled Quality Control Sample [10, 17, 82] -- 5.4. Long-Term Reference Material [17, 85] -- 5.5. Extraction Blank Samples -- 6. Analytical Robustness for Small-Scale and Large-Scale Studies -- 7. Data Processing -- 8. Metabolite Annotation and Identification -- 9. Exemplar Applications -- Acknowledgments -- References -- Chapter 4: GC-MS-Based Metabolic Phenotyping -- 1. Introduction -- 2. GC-MS as an Analytical Technique -- 3. GC-MS Metabolic Phenotyping Workflow -- 4. Sample Preparation for GC-MS-Based Metabolic Phenotyping -- 5. Data Processing -- 6. Statistical Evaluation. , 7. Biochemical Interpretation -- 8. GC-MS Troubleshooting -- 9. Clinical Applications -- 10. Conclusion -- References -- Chapter 5: Metabolic Phenotyping Using Capillary Electrophoresis Mass Spectrometry -- 1. Introduction -- 1.1. Instrumentation -- 1.2. Principles of Capillary Zone Electrophoresis -- 1.3. Conditions That Can Be Modified to Optimize the Separation and Limitations When Coupled to MS -- 1.4. Strengths -- 1.5. Weaknesses -- 1.6. Coupling to MS -- 2. Workflow -- 2.1. Design -- 2.2. Sample -- 2.3. Separation and Detection -- 2.3.1. Materials -- 2.3.2. Capillary Preparation -- 2.3.3. CE-MS Method -- 2.4. Data -- 2.5. Statistics -- 2.6. Identification -- 2.7. Meaning -- 3. Applications -- 3.1. Cancer -- 3.2. Metabolic Disorders -- 3.3. Neurodegenerative Diseases and Brain Disorders -- 3.4. Others -- 3.4.1. Infectious Diseases -- 3.4.2. Alcohol Abuse -- 3.4.3. Clinical Approaches -- 4. Conclusions and Future Trends -- References -- Chapter 6: Supercritical Fluid Chromatography for Metabolic Phenotyping: Potential and Applications -- 1. Introduction -- 2. Application of SFC to Nonpolar Metabolite Analysis -- 2.1. Lipids -- 2.2. Bile and Bile Acids -- 2.3. Applications of SFC to Polar Metabolite Analysis -- 3. Conclusion -- References -- Chapter 7: The iKnife: Development and Clinical Applications of Rapid Evaporative Ionization Mass Spectrometry -- Abbreviations -- 1. Introduction -- 2. Basic Principles of Mass Spectrometry -- 3. Nonambient and Ambient Mass Spectrometry -- 4. Development of Rapid Evaporative Ionization Mass Spectrometry (REIMS) (the iKnife) -- 5. Model Generation: The Concept of Profiling -- 6. Clinical Applications -- 6.1. Oncological Surgery: Introduction -- 6.2. Breast Cancer: The iKnife as a Diagnostic and Margin Assessment Tool -- 6.3. Brain Tumors: The iKnife as a Diagnostic and Extent of Resection Assessment Tool. , 6.4. Gastroenterology: The iEndoscope -- 7. Microbiology Applications -- 8. Conclusions -- References -- Chapter 8: Univariate Statistical Modeling, Multiple Testing Correction, and Visualization in Metabolome-Wide Association ... -- 1. Metabolic Phenotyping: Opportunities and Challenges -- 1.1. Application to Public Health Studies -- 1.2. Overview of the Analytical Pipeline in Metabolic Phenotyping -- 2. Metabolome-Wide Association Study: Univariate Approaches -- 2.1. Imputation and Normalization -- 2.2. Statistical Analyses Using Univariate Approaches -- 2.3. Linear and Generalized Linear Models -- 2.4. (Generalized) Linear Mixed Models -- 2.5. Meta-Analyses -- 3. Metabolome-Wide Significance Level -- 3.1. FWER and FDR Control -- 3.2. Defining the Metabolome-Wide Significance Level -- 4. Results Prioritization and Visualization -- 5. Conclusions -- Acknowledgment -- References -- Chapter 9: Multivariate Statistical Methods for Metabolic Phenotyping -- 1. Introduction -- 1.1. Mathematical Notations -- 1.2. Data Transformations -- 1.2.1. Mean Centering -- 1.2.2. Autoscaling -- 1.2.3. Generalized Equation for Scaling -- 1.3. Data Set Partitioning -- 2. Unsupervised Pattern Recognition -- 2.1. Unsupervised Methods for Dimension Reduction and Multivariate Projections -- 2.1.1. Principal Component Analysis -- 2.1.2. Projection Pursuit and Independent Component Analysis -- 2.1.3. Kernel Principal Component Analysis -- 2.1.4. Sparse Principal Component Analysis -- 2.2. Outlier Detection -- 2.2.1. Hotelling's T2 -- 2.2.2. Quality Control Samples -- 2.2.3. Robust Principal Component Analysis -- 2.3. Other Data Exploration Approaches for Specific Experimental Designs and Multiway Analysis -- 2.3.1. Multiway Methods -- 2.3.2. Multivariate Curve Resolution -- 2.3.3. Nonnegative Principal Component Analysis and Nonnegative Matrix Factorization. , 3. Supervised Pattern Recognition and Multivariate Regression -- 3.1. Discriminant and Cluster Analysis -- 3.1.1. Linear Discriminant Analysis -- 3.1.2. Quadratic Discriminant Analysis -- 3.1.3. k-Nearest Neighbors -- 3.1.4. k-Means, k-Medoids, and Fuzzy Alternatives -- 3.2. Multivariate Regression -- 3.2.1. Multiple Linear Regression -- 3.2.2. Principal Component Regression -- 3.2.3. Partial Least Squares -- 3.2.4. Kernel Partial Least Squares -- 3.2.5. Orthogonal Signal Correction-Filtered Partial Least Squares and Orthogonal Projections to Latent Structures -- 3.2.6. Two-Block Orthogonal Projections to Latent Structures -- 3.2.7. Kernel Orthogonal Projections to Latent Structures -- 3.3. Penalized Regression -- 3.3.1. Ridge Regression and Kernel Ridge Regression -- 3.3.2. Sparse Penalized Regression Using the Lasso -- 3.3.3. The Elastic Net, a Weighted Average of Lasso and Ridge Regression -- 3.4. Nonparametric Machine Learning and Recursive Partitioning Methods -- 3.4.1. Support Vector Machines -- 3.4.2. Variable Importance in Linear Support Vector Machines -- 3.4.3. Uncovering Variable Importance for Nonlinear Kernel-Based Models -- 3.4.4. Decision Trees for Classification -- 3.4.5. Random Forests -- 3.5. Prediction Error Rates and Model Fits -- 3.6. Other Supervised Methods and Those for Specific Experimental Designs -- 3.6.1. ANOVA-Based Decompositions -- 3.6.2. Bayesian Approaches -- 3.6.3. Deep Learning Algorithms -- 4. Statistical Approaches for Biomarker Identification -- 4.1. Statistical Total Correlation Spectroscopy -- 4.2. Correlation Networks of Biological Coregulation -- 4.3. Subset Optimization by Reference Matching and the Statistical Use of Second Dimension Data -- 4.4. Statistical Hetero Spectroscopy and Data Fusion -- 5. Conclusion -- References -- Chapter 10: Data-Driven Visualizations in Metabolic Phenotyping. , 1. Visualizing Metabolic Phenotyping Data and Analysis.

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