Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic provides a way forward in understanding complete disease etiology and prognosis. This unique book gives a comprehensive understanding of multi-omics technology and its applications in understanding complex human health and disease and not only introduces the technology, but also its use for biomarker identification, drug discovery, and disease prognostication. A comprehensive understanding of human health and diseases, particularly cancer, requires knowledge of molecular biomarkers at a multi-omics level, including genome, epigenome, transcriptome, proteome, and metabolome, which is meagerly available or highly lacking among the students and scientific researchers. This book covers all the aspects of multi-omics technology and how this technology can be implemented into scientific research and discovery.Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic is the first resource to present the latest information on currently utilized multi-omics technology for big data interpretation and their applications in understanding the complex human pathobiology. This is the perfect resource for researchers, academia, students, as well as the industrial audience.
1. Chapter 1. Introduction to multi-omics technology - p. 1 2. Chapter 2. Multi-omics data tools and integration approaches - p. 13 3. Chapter 3. Navigating the multifaceted landscape of omics - p. 49 4. Chapter 4. Multiomics in cancer research - p. 69 5. Chapter 5. Multi-omics for disease subtyping and classification - p. 79 6. Chapter 6. Multi-omics and biomarker discoveries in Alzheimer's disease - p. 135 7. Chapter 7. Multi-omics and drug development - p. 147 8. Chapter 8. Charting the cancer landscape with multiomic technologies - p. 173 9. Chapter 9. Multi-omics data for machine learning algorithms - p. 197 10. Chapter 10. Challenges with multi-omics data integration - p. 223 11. Chapter 11. Multi-omics technology and cardiovascular diseases - p. 243 12. Index - p. 261
Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic provides a way forward in understanding complete disease etiology and prognosis. This unique book gives a comprehensive understanding of multi-omics technology and its applications in understanding complex human health and disease and not only introduces the technology, but also its use for biomarker identification, drug discovery, and disease prognostication. A comprehensive understanding of human health and diseases, particularly cancer, requires knowledge of molecular biomarkers at a multi-omics level, including genome, epigenome, transcriptome, proteome, and metabolome, which is meagerly available or highly lacking among the students and scientific researchers. This book covers all the aspects of multi-omics technology and how this technology can be implemented into scientific research and discovery.Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic is the first resource to present the latest information on currently utilized multi-omics technology for big data interpretation and their applications in understanding the complex human pathobiology. This is the perfect resource for researchers, academia, students, as well as the industrial audience.
1. Chapter 1. Introduction to multi-omics technology - p. 1 2. Chapter 2. Multi-omics data tools and integration approaches - p. 13 3. Chapter 3. Navigating the multifaceted landscape of omics - p. 49 4. Chapter 4. Multiomics in cancer research - p. 69 5. Chapter 5. Multi-omics for disease subtyping and classification - p. 79 6. Chapter 6. Multi-omics and biomarker discoveries in Alzheimer's disease - p. 135 7. Chapter 7. Multi-omics and drug development - p. 147 8. Chapter 8. Charting the cancer landscape with multiomic technologies - p. 173 9. Chapter 9. Multi-omics data for machine learning algorithms - p. 197 10. Chapter 10. Challenges with multi-omics data integration - p. 223 11. Chapter 11. Multi-omics technology and cardiovascular diseases - p. 243 12. Index - p. 261
