Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene expression studies. In 21 chapters Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications describes how AI and DL have become increasingly useful in genetics and genomics research where both play a crucial role by accelerating research, improving the understanding of the human genome, and enabling personalized healthcare. From the fundamentals concepts and practical applications of deep learning algorithms to a wide range of challenging problems from genetics and genomics, Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications creates a better knowledge of the biological and genetics mechanisms behind disease illnesses and improves the forecasting abilities using the different methodologies described. This title offers a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning-based approaches in genetics and genomics, helping researchers process and interpret vast amounts of genetic data, identify patterns, and make discoveries that would be challenging or impossible using traditional methods.
1. Chapter 2 : Introduction to deep learning for genomics - p. Page 29 2. Chapter 3 : Foundations and applications of computational genomics - p. Page 55 3. Chapter 4 : Decoding DNA: Deep learning's impact on genomic exploration - p. Page 69 4. Chapter 5 : AI and deep learning in cancer genomics - p. Page 93 5. Chapter 6 : Unravelling the recent developments in applications and challenges of AI in cancer biology: An overview - p. Page 114 6. Chapter 7 : Unlocking the potential of deep learning for oncological sequence analysis: A review - p. Page 140 7. Chapter 8 : Deep learning in medical genetics: A review - p. Page 157 8. Chapter 9 : Navigating the genomic landscape: A deep dive into clinical genetics with deep learning - p. Page 179 9. Chapter 10 : Advancing clinical genomics: Bridging the gap between deep learning models and interpretability for improved decision support - p. Page 212 10. Chapter 11 : Deep learning in clinical genomics-based cancer diagnosis - p. Page 240 11. Chapter 12 : Deep learning in predictive medicine: Current state of the art - p. Page 254 12. Chapter 13 : Applications of AI in cancer genomics: A way toward intelligent decision systems in healthcare - p. Page 285 13. Chapter 14 : The role of deep learning in drug discovery - p. Page 303 14. Chapter 15 : Deep learning applications in genomics-based toxicology assessment - p. Page 322 15. Chapter 16 : The revolutionary impact of deep learning in transcriptomics and gene expression analysis: A genomic paradigm shift - p. Page 341 16. Chapter 17 : Data-driven genomics: A triad of big data, cloud, and IoT in genomics research - p. Page 360 17. Chapter 18 : Deep learning in variant detection and annotation - p. Page 379 18. Chapter 19 : Inequality in genetic healthcare: Bridging gaps with deep learning innovations in low-income and middle-income countries - p. Page 395 19. Chapter 20 : Analysis of genetic and clinical features in neuro disorders using deep learning models - p. Page 407 20. Index - p. Page 429
Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene expression studies. In 21 chapters Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications describes how AI and DL have become increasingly useful in genetics and genomics research where both play a crucial role by accelerating research, improving the understanding of the human genome, and enabling personalized healthcare. From the fundamentals concepts and practical applications of deep learning algorithms to a wide range of challenging problems from genetics and genomics, Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications creates a better knowledge of the biological and genetics mechanisms behind disease illnesses and improves the forecasting abilities using the different methodologies described. This title offers a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning-based approaches in genetics and genomics, helping researchers process and interpret vast amounts of genetic data, identify patterns, and make discoveries that would be challenging or impossible using traditional methods.
1. Chapter 2 : Introduction to deep learning for genomics - p. Page 29 2. Chapter 3 : Foundations and applications of computational genomics - p. Page 55 3. Chapter 4 : Decoding DNA: Deep learning's impact on genomic exploration - p. Page 69 4. Chapter 5 : AI and deep learning in cancer genomics - p. Page 93 5. Chapter 6 : Unravelling the recent developments in applications and challenges of AI in cancer biology: An overview - p. Page 114 6. Chapter 7 : Unlocking the potential of deep learning for oncological sequence analysis: A review - p. Page 140 7. Chapter 8 : Deep learning in medical genetics: A review - p. Page 157 8. Chapter 9 : Navigating the genomic landscape: A deep dive into clinical genetics with deep learning - p. Page 179 9. Chapter 10 : Advancing clinical genomics: Bridging the gap between deep learning models and interpretability for improved decision support - p. Page 212 10. Chapter 11 : Deep learning in clinical genomics-based cancer diagnosis - p. Page 240 11. Chapter 12 : Deep learning in predictive medicine: Current state of the art - p. Page 254 12. Chapter 13 : Applications of AI in cancer genomics: A way toward intelligent decision systems in healthcare - p. Page 285 13. Chapter 14 : The role of deep learning in drug discovery - p. Page 303 14. Chapter 15 : Deep learning applications in genomics-based toxicology assessment - p. Page 322 15. Chapter 16 : The revolutionary impact of deep learning in transcriptomics and gene expression analysis: A genomic paradigm shift - p. Page 341 16. Chapter 17 : Data-driven genomics: A triad of big data, cloud, and IoT in genomics research - p. Page 360 17. Chapter 18 : Deep learning in variant detection and annotation - p. Page 379 18. Chapter 19 : Inequality in genetic healthcare: Bridging gaps with deep learning innovations in low-income and middle-income countries - p. Page 395 19. Chapter 20 : Analysis of genetic and clinical features in neuro disorders using deep learning models - p. Page 407 20. Index - p. Page 429
