Buch, Englisch, 268 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 453 g
Buch, Englisch, 268 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 453 g
ISBN: 978-1-032-87833-1
Verlag: Taylor & Francis Ltd
The objective of the book is to use advanced deep learning techniques to unlock the complexities of genomic data. It reveals hidden and unknown patterns, to improve our understanding the role of genetics in diseases, speeding up drug discovery processes, illuminating evolutionary trajectories, and dealing with the challenges posed by large genomic datasets. It also addresses ethical concerns, provides real-world applications, reviews future frontiers like as quantum computing and multi-omics integration, and presents a thorough picture of the genetic environment. Overall, the book aims to enhance our ability to use genomics for more precise forecasts, personalised therapies, and a better understanding of the underlying genetic fabric that defines life.
Zielgruppe
Academic and Postgraduate
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
- Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
- Mathematik | Informatik EDV | Informatik Technische Informatik Quantencomputer, DNA-Computing
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
Weitere Infos & Material
1) Introduction: Genomic Landscape & Deep Learning Rise
2) Unraveling Genetic Code: Basics & Deep Learning Role
3) Genomic Data Deluge: Handling Big Data with DL
4) Mining Biological Gold: DL in Disease, Drug, Evolution Insights
5) Navigating Genome: Neural Networks, Transfer Learning, Explainability
6) Ethical Considerations: Privacy, Fairness, Bias in Genomic Data
7) Blockchain Integration in Genomic Deep Learning: Securing, Validating, and Sharing Genetic Insights
8) Future Horizons: Multi-Omics, Quantum Computing, Collaboration
9) Case Studies: Real-world Apps, Lessons
10) Challenges & Opportunities: Technical Hurdles, Robust Models
11) Conclusion: Recap & Genomic Frontier Ahead