Buch, Englisch, 187 Seiten, Format (B × H): 173 mm x 246 mm, Gewicht: 532 g
Reihe: Synthesis Lectures on Engineering, Science, and Technology
Methods, Applications, and Clinical Translation
Buch, Englisch, 187 Seiten, Format (B × H): 173 mm x 246 mm, Gewicht: 532 g
Reihe: Synthesis Lectures on Engineering, Science, and Technology
ISBN: 978-3-032-25485-6
Verlag: Springer
This book provides a comprehensive and accessible exploration of how Artificial Intelligence (AI) is transforming the field of Genomics and personalized medicine. The book brings together the latest methods, tools, and real-world applications that enable scientists and clinicians to interpret complex genetic data, discover disease-causing patterns, and design precision therapies. Covering topics from deep learning and multi-omics integration to ethical and regulatory considerations, the book bridges the gap between computational innovation and clinical translation. Designed for researchers, students, healthcare professionals, and biotech innovators, this book offers clear explanations, illustrative case studies, and forward-looking insights into how AI is shaping the future of medicine and human health.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
Weitere Infos & Material
Introduction.- Genomics in the Age of Artificial Intelligence.- Data Foundations: Sequencing, Multi-Omics, and Data Quality.- Classical Machine Learning Methods in Genomics.- Deep Learning Architectures for Genomic Sequences and Structures.- Graph Neural Networks and Biological Networks.- Self-Supervised and Foundation Models in Genomic Research.- Integrative Multi-Omics Modeling and Data Fusion.- Interpretability, Explainability, and Causality in Genomic AI.- Federated Learning and Privacy-Preserving Genomics.- AI for Clinical Genomics: Diagnostics and Prognostics.- AI-Driven Drug Discovery and Pharmacogenomics.- Population Genomics and Public Health Applications.- Ethical, Legal, and Societal Implications in Genomic AI.- Future Perspectives: Generative Models, Digital Twins, and Personalized Medicine.- Conclusion.




