Integrative Omics: Concepts, Methodology and Applications provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods to solve problems for the rationalization of genotype to phenotype relationships. The reference includes a range of chapters in a systemic ‘step by step’ manner, which begins with the basic concepts from Omic to Multi Integrative Omics approaches, followed by their full range of approaches, applications, emerging trends, and future trends.
All key areas of Omics are covered including biological databases, sequence alignment, pharmacogenomics, nutrigenomics and microbial omics, integrated omics for Food Science and Identification of genes associated with disease, clinical data integration and data warehousing, translational omics as well as omics technology policy and society research.
Integrative Omics: Concepts, Methodology and Applications highlights the recent concepts, methodologies, advancements in technologies and is also well-suited for researchers from both academic and industry background, undergraduate and graduate students who are mainly working in the area of computational systems biology, integrative omics and translational science. The book bridges the gap between biological sciences, physical sciences, computer science, statistics, data science, information technology and mathematics by presenting content specifically dedicated to mathematical models of biological systems.
1. Integrative Omics - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Table of Contents - p. ii 5. Copyright Page - p. iii 6. Dedication - p. iv 7. List of contributors - p. xvii 8. Preface - p. xix 9. Chapter 1 From omic to multi-integrative omics approach - p. 2 10. Chapter 2 Types of omics data - p. 14 11. Chapter 3 Biological omics databases and tools - p. 36 12. Chapter 4 Systematic benchmarking of omics computational tools - p. 56 13. Chapter 5 Pharmacogenomics, nutrigenomics, and microbial omics - p. 86 14. Chapter 6 Proteomics - p. 100 15. Chapter 7 Foodomics - p. 122 16. Chapter 8 Vaccinomics - p. 134 17. Chapter 9 Integrative omics approach for identification of genes associated with disease - p. 144 18. Chapter 10 Integrative omics approaches for identification of biomarkers - p. 158 19. Chapter 11 Omics approach for personalized and diagnostics medicine - p. 176 20. Chapter 12 Role of bioinformatics in genome analysis - p. 188 21. Chapter 13 Data management in cross-omics - p. 202 22. Chapter 14 Omics and clinical data integration and data warehousing - p. 226 23. Chapter 15 Integrative omics data mining - p. 238 24. Chapter 16 Data science and analytics, modeling, simulation, and issues of omics dataset - p. 258 25. Chapter 17 Emerging trends in translational omics - p. 280 26. Chapter 18 Omics technologies for crop improvement - p. 294 27. Chapter 19 Ecology and environmental omics - p. 316 28. Chapter 20 Current trends and approaches in clinical metagenomics - p. 334 29. Chapter 21 Biomolecular networks - p. 352 30. Chapter 22 Machine learning fundamentals to explore complex omics data - p. 370 31. Chapter 23 Omics technology policy and society research - p. 380 32. Index - p. 401
Integrative Omics: Concepts, Methodology and Applications provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods to solve problems for the rationalization of genotype to phenotype relationships. The reference includes a range of chapters in a systemic ‘step by step’ manner, which begins with the basic concepts from Omic to Multi Integrative Omics approaches, followed by their full range of approaches, applications, emerging trends, and future trends.
All key areas of Omics are covered including biological databases, sequence alignment, pharmacogenomics, nutrigenomics and microbial omics, integrated omics for Food Science and Identification of genes associated with disease, clinical data integration and data warehousing, translational omics as well as omics technology policy and society research.
Integrative Omics: Concepts, Methodology and Applications highlights the recent concepts, methodologies, advancements in technologies and is also well-suited for researchers from both academic and industry background, undergraduate and graduate students who are mainly working in the area of computational systems biology, integrative omics and translational science. The book bridges the gap between biological sciences, physical sciences, computer science, statistics, data science, information technology and mathematics by presenting content specifically dedicated to mathematical models of biological systems.
1. Integrative Omics - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Table of Contents - p. ii 5. Copyright Page - p. iii 6. Dedication - p. iv 7. List of contributors - p. xvii 8. Preface - p. xix 9. Chapter 1 From omic to multi-integrative omics approach - p. 2 10. Chapter 2 Types of omics data - p. 14 11. Chapter 3 Biological omics databases and tools - p. 36 12. Chapter 4 Systematic benchmarking of omics computational tools - p. 56 13. Chapter 5 Pharmacogenomics, nutrigenomics, and microbial omics - p. 86 14. Chapter 6 Proteomics - p. 100 15. Chapter 7 Foodomics - p. 122 16. Chapter 8 Vaccinomics - p. 134 17. Chapter 9 Integrative omics approach for identification of genes associated with disease - p. 144 18. Chapter 10 Integrative omics approaches for identification of biomarkers - p. 158 19. Chapter 11 Omics approach for personalized and diagnostics medicine - p. 176 20. Chapter 12 Role of bioinformatics in genome analysis - p. 188 21. Chapter 13 Data management in cross-omics - p. 202 22. Chapter 14 Omics and clinical data integration and data warehousing - p. 226 23. Chapter 15 Integrative omics data mining - p. 238 24. Chapter 16 Data science and analytics, modeling, simulation, and issues of omics dataset - p. 258 25. Chapter 17 Emerging trends in translational omics - p. 280 26. Chapter 18 Omics technologies for crop improvement - p. 294 27. Chapter 19 Ecology and environmental omics - p. 316 28. Chapter 20 Current trends and approaches in clinical metagenomics - p. 334 29. Chapter 21 Biomolecular networks - p. 352 30. Chapter 22 Machine learning fundamentals to explore complex omics data - p. 370 31. Chapter 23 Omics technology policy and society research - p. 380 32. Index - p. 401
