附註:Includes bibliographical references and index.
Chapter 1. Advancing zero-shot learning with fully connected weighted bipartite graphs in machine learning -- Chapter 2. Bioinformatics in agriculture and ecology using few-shots learning from field to conservation -- Chapter 3. An overview and analysis of machine learning classification algorithms in healthcare -- Chapter 4. Ethical and legal considerations in machine learning: promoting responsible data use in bioinformatics -- Chapter 5. A comprehensive analysis ofthe health effects of 5G radiation -- Chapter 6. Bridging bytes and biology-advanced learning and bioinformatics in innovative drug discovery -- Chapter 7. Challenges and limitations of few-shot and zero-shot learning -- Chapter 8. Unveiling the potential: a comprehensive exploration of deep learning and transfer learning techniques in bioinformatics -- Chapter 9. Unlocking the future of healthcare: biomarkers and personalized medicine -- Chapter 10. Unveiling the world of bioinformatics -- Chapter 11. Ethical considerations in sharing patient data: a systematic review -- Chapter 12. Exploration of deep learning and transfer learning in bioinformatics -- Chapter 13. Exploration of deep learning and transfer learning techniques in bioinformatics -- Chapter 14. Unlocking the future of healthcare: biomarkers and personalized medicine -- Chapter 15. Machine learning's potential in shaping the future of bioinformatics research -- Chapter 16. Exploringfew-shot learning approaches for bioinformatics advancements -- Chapter 17. Introduction to bioinformatics and machine learning -- Chapter 18. Learning from scarcity: unlocking healthcare insights with few-shot machine learning.
摘要:"Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists' ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complexbiological challenges.The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences."--
