Omics Approaches and Technologies in COVID-19 discusses current and in development technologies used to fight the SARS-CoV-2 pandemic. Based on the research developed since the virus' outbreak, experts from around the world present perspectives on how recent technologies can help diagnose, treat and control. The book discusses topics such as genomics, meta-genomics, nutrigenomics, transcriptomics, proteomics, metabolomics, epigenomics, multi-omics and pan-genomics approaches, CRISPR, host-pathogen interactions, systems biology, artificial intelligence and machine learning applied to COVID-19, bioinformatics, mathematical modeling, and big data. In addition, it presents smart technologies to control SARS-CoV-2 transmission and technologies for the prediction of patients’ health conditions and treatment outcomes.
This is a valuable resource for graduate students, researchers and members of the biomedical field who are interested in learning more about novel approaches and technologies used to tackle the COVID-19 pandemic.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xix 8. About the editors - p. xxii 9. Preface - p. xxii 10. Section A: Omics - p. xxii 11. Chapter 1. Omics approaches in COVID-19: An overview - p. 3 12. Chapter 2. Genomics, metagenomics, and pan-genomics approaches in COVID-19 - p. 23 13. Chapter 3. Genotype and phenotype correlations in COVID-19 - p. 41 14. Chapter 4. Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives - p. 63 15. Chapter 5. Metabolites and metabolomics in COVID-19 - p. 87 16. Chapter 6. Host-pathogen protein-protein interactions and interactomics in COVID-19 - p. 101 17. Chapter 7. Currently available COVID-19 management options - p. 111 18. Chapter 8. Transcriptomic approaches in COVID-19: From infection to vaccines - p. 125 19. Chapter 9. miRNAomics in COVID-19 - p. 145 20. Chapter 10. Epigenetic features, methods, and implementations associated with COVID-19 - p. 161 21. Chapter 11. Nutrigenetics and nutrition aspects in COVID-19 - p. 177 22. Chapter 12. COVID-19 phenomics - p. 199 23. Chapter 13. Applications of genetic engineering in COVID-19 - p. 219 24. Chapter 14. CRISPR-based assays for rapid detection of SARS-CoV-2 - p. 239 25. Section B: Artificial intelligence and bioinformatics - p. 242 26. Chapter 15. Emergence and dynamics of COVID-19 and future pandemics - p. 245 27. Chapter 16. Artificial intelligence in COVID-19 - p. 255 28. Chapter 17. Applications of mathematical modeling and simulation in COVID-19 - p. 275 29. Chapter 18. In silico disease modeling for COVID-19 - p. 291 30. Chapter 19. Systems biology in COVID-19 - p. 301 31. Chapter 20. Computational approaches for drug discovery against COVID-19 - p. 321 32. Chapter 21. Computational approaches in COVID-19 vaccine development - p. 339 33. Chapter 22. Applications of multiomics data in COVID-19 - p. 351 34. Chapter 23. Publicly available resources in COVID-19 research and their applications - p. 367 35. Section C: Smart and emerging technologies - p. 385 36. Chapter 24. Emerging technologies for COVID-19, diagnosis, prevention, and management - p. 389 37. Chapter 25. Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring - p. 405 38. Chapter 26. Application of big data analytics in the COVID-19 pandemic: Selected problems - p. 427 39. Index - p. 431
Omics Approaches and Technologies in COVID-19 discusses current and in development technologies used to fight the SARS-CoV-2 pandemic. Based on the research developed since the virus' outbreak, experts from around the world present perspectives on how recent technologies can help diagnose, treat and control. The book discusses topics such as genomics, meta-genomics, nutrigenomics, transcriptomics, proteomics, metabolomics, epigenomics, multi-omics and pan-genomics approaches, CRISPR, host-pathogen interactions, systems biology, artificial intelligence and machine learning applied to COVID-19, bioinformatics, mathematical modeling, and big data. In addition, it presents smart technologies to control SARS-CoV-2 transmission and technologies for the prediction of patients’ health conditions and treatment outcomes.
This is a valuable resource for graduate students, researchers and members of the biomedical field who are interested in learning more about novel approaches and technologies used to tackle the COVID-19 pandemic.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xix 8. About the editors - p. xxii 9. Preface - p. xxii 10. Section A: Omics - p. xxii 11. Chapter 1. Omics approaches in COVID-19: An overview - p. 3 12. Chapter 2. Genomics, metagenomics, and pan-genomics approaches in COVID-19 - p. 23 13. Chapter 3. Genotype and phenotype correlations in COVID-19 - p. 41 14. Chapter 4. Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives - p. 63 15. Chapter 5. Metabolites and metabolomics in COVID-19 - p. 87 16. Chapter 6. Host-pathogen protein-protein interactions and interactomics in COVID-19 - p. 101 17. Chapter 7. Currently available COVID-19 management options - p. 111 18. Chapter 8. Transcriptomic approaches in COVID-19: From infection to vaccines - p. 125 19. Chapter 9. miRNAomics in COVID-19 - p. 145 20. Chapter 10. Epigenetic features, methods, and implementations associated with COVID-19 - p. 161 21. Chapter 11. Nutrigenetics and nutrition aspects in COVID-19 - p. 177 22. Chapter 12. COVID-19 phenomics - p. 199 23. Chapter 13. Applications of genetic engineering in COVID-19 - p. 219 24. Chapter 14. CRISPR-based assays for rapid detection of SARS-CoV-2 - p. 239 25. Section B: Artificial intelligence and bioinformatics - p. 242 26. Chapter 15. Emergence and dynamics of COVID-19 and future pandemics - p. 245 27. Chapter 16. Artificial intelligence in COVID-19 - p. 255 28. Chapter 17. Applications of mathematical modeling and simulation in COVID-19 - p. 275 29. Chapter 18. In silico disease modeling for COVID-19 - p. 291 30. Chapter 19. Systems biology in COVID-19 - p. 301 31. Chapter 20. Computational approaches for drug discovery against COVID-19 - p. 321 32. Chapter 21. Computational approaches in COVID-19 vaccine development - p. 339 33. Chapter 22. Applications of multiomics data in COVID-19 - p. 351 34. Chapter 23. Publicly available resources in COVID-19 research and their applications - p. 367 35. Section C: Smart and emerging technologies - p. 385 36. Chapter 24. Emerging technologies for COVID-19, diagnosis, prevention, and management - p. 389 37. Chapter 25. Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring - p. 405 38. Chapter 26. Application of big data analytics in the COVID-19 pandemic: Selected problems - p. 427 39. Index - p. 431
