Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences.
1. Deep Learning in Genetics and Genomics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. Contributors - p. Page v 8. About the editor - p. Page x 9. Preface - p. Page xi 10. Acknowledgments - p. Page xii 11. About the book - p. Page xiii 12. List of Illustrations - p. Page xiv 13. List of Tables - p. Page xv 14. Chapter 1 : Deep learning in predictive medicine exemplified by AI-mediated flu surveillance in USA - p. Page xv 15. Chapter 2 : Toward equitable precision medicine: Investigating the transferability of deep learning models in clinical genetics across diverse populations - p. Page 29 16. Chapter 3 : Deep learning insights into transcriptomics and gene expression patterns analysis - p. Page 43 17. Chapter 4 : Role of artificial intelligence in clinical cancer genomics for oncology - p. Page 58 18. Chapter 5 : Deep learning approaches for interpreting Non-coding regions in Ovarian cancer - p. Page 67 19. Chapter 6 : Advancements in artificial intelligence-driven spatial transcriptomics: Decoding cellular complexity - p. Page 81 20. Chapter 7 : Advancements in clinical decision support through deep learning approaches in genetic diagnostics - p. Page 104 21. Chapter 8 : Neural architectures for genomic understanding: Deep dive into epigenome and chromatin structure - p. Page 125 22. Chapter 9 : Deep learning in personalized genomics and gene editing - p. Page 144 23. Chapter 10 : Deep learning–based model for prediction of prognostic genes of breast cancer using transcriptomic data - p. Page 164 24. Chapter 11 : Genomic image analysis: Bridging genomics and advanced imaging - p. Page 189 25. Chapter 12 : Qualitative study on steganography of genomic image data for secure data transmission using deep learning models - p. Page 215 26. Chapter 13 : Generative artificial intelligence in genetics: A comprehensive review - p. Page 233 27. Chapter 14 : Integrating computational biology and multiomics data for precision medicine in personalized cancer treatment - p. Page 246 28. Chapter 15 : Deep generative models in utilitarian and metamorphic genomics—Intellectual benefits - p. Page 266 29. Chapter 16 : Bridging the gap: Understanding genetic discoveries through explainable artificial intelligence - p. Page 297 30. Chapter 17 : Explainable Artificial Intelligence in genetics: A case study - p. Page 310 31. Chapter 18 : Deep learning in predicting genetic disorders: A case study of diabetic kidney disease - p. Page 325 32. Chapter 19 : Artificial intelligence and deep learning in single-cell omics data analysis: A case study - p. Page 344 33. Chapter 20 : Deep learning for network building and analysis of biological networks: A case study - p. Page 376 34. Chapter 21 : Transformer networks and autoencoders in genomics and genetic data interpretation: A case study - p. Page 395 35. Index - p. Page 420
Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences.
1. Deep Learning in Genetics and Genomics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. Contributors - p. Page v 8. About the editor - p. Page x 9. Preface - p. Page xi 10. Acknowledgments - p. Page xii 11. About the book - p. Page xiii 12. List of Illustrations - p. Page xiv 13. List of Tables - p. Page xv 14. Chapter 1 : Deep learning in predictive medicine exemplified by AI-mediated flu surveillance in USA - p. Page xv 15. Chapter 2 : Toward equitable precision medicine: Investigating the transferability of deep learning models in clinical genetics across diverse populations - p. Page 29 16. Chapter 3 : Deep learning insights into transcriptomics and gene expression patterns analysis - p. Page 43 17. Chapter 4 : Role of artificial intelligence in clinical cancer genomics for oncology - p. Page 58 18. Chapter 5 : Deep learning approaches for interpreting Non-coding regions in Ovarian cancer - p. Page 67 19. Chapter 6 : Advancements in artificial intelligence-driven spatial transcriptomics: Decoding cellular complexity - p. Page 81 20. Chapter 7 : Advancements in clinical decision support through deep learning approaches in genetic diagnostics - p. Page 104 21. Chapter 8 : Neural architectures for genomic understanding: Deep dive into epigenome and chromatin structure - p. Page 125 22. Chapter 9 : Deep learning in personalized genomics and gene editing - p. Page 144 23. Chapter 10 : Deep learning–based model for prediction of prognostic genes of breast cancer using transcriptomic data - p. Page 164 24. Chapter 11 : Genomic image analysis: Bridging genomics and advanced imaging - p. Page 189 25. Chapter 12 : Qualitative study on steganography of genomic image data for secure data transmission using deep learning models - p. Page 215 26. Chapter 13 : Generative artificial intelligence in genetics: A comprehensive review - p. Page 233 27. Chapter 14 : Integrating computational biology and multiomics data for precision medicine in personalized cancer treatment - p. Page 246 28. Chapter 15 : Deep generative models in utilitarian and metamorphic genomics—Intellectual benefits - p. Page 266 29. Chapter 16 : Bridging the gap: Understanding genetic discoveries through explainable artificial intelligence - p. Page 297 30. Chapter 17 : Explainable Artificial Intelligence in genetics: A case study - p. Page 310 31. Chapter 18 : Deep learning in predicting genetic disorders: A case study of diabetic kidney disease - p. Page 325 32. Chapter 19 : Artificial intelligence and deep learning in single-cell omics data analysis: A case study - p. Page 344 33. Chapter 20 : Deep learning for network building and analysis of biological networks: A case study - p. Page 376 34. Chapter 21 : Transformer networks and autoencoders in genomics and genetic data interpretation: A case study - p. Page 395 35. Index - p. Page 420
