Applying Artificial Intelligence to Computational Biology and Medical Informatics explores the transformative role of AI and machine learning in modern biomedical research and healthcare. The book covers foundational concepts in AI/ML, computational biology, and medical informatics, followed by in-depth chapters on medical imaging, network biology, chemoinformatics, and public health. Other sections address ethical and societal implications, interpretable AI, and real-world case studies, making complex topics accessible through clear language and structured content. Designed for early-career researchers, students, and professionals without prior expertise in computer science or health sciences, this book provides a progressive learning path from basic to intermediate levels.
Readers benefit from practical examples, online resources, and a coherent chapter structure that supports both academic study and applied research.
1. 1. Introduction to AI/ML, computational biology, and medical informatics - p. 1 2. 2. AI/ML applications in medical imaging - p. 17 3. 3. Natural language technologies in biomedical domain - p. 41 4. 4. AI/ML in chemoinformatics - p. 73 5. 5. Deep learning methods for network biology - p. 97 6. 6. Probabilistic optimization of ML for heart disease prediction - p. 115 7. 7. The need for interpretable and explainable deep learning data in health care - p. 137 8. 8. Using hybrid models in healthcare data - p. 161 9. 9. Improving disease prediction by integrating multiple ML and optimization techniques - p. 185 10. 10. Ethical, societal, and legal issues in AI/ML for healthcare - p. 207 11. 11. Deep learning in gait abnormality detection: Principles and illustrations - p. 223 12. 12. Broad applications of network embedding in computational biology, genomics, medicine, and health - p. 245 13. 13. AI use in clinical prediction - p. 275 14. 14. AI/ML for medical informatics and public health - p. 289 15. 15. AI in medical imaging for developing countries: Challenges and opportunities - p. 307 16. 16. AI applications in disease diagnosis and treatment: New applications - p. 345 17. Index - p. 363
Applying Artificial Intelligence to Computational Biology and Medical Informatics explores the transformative role of AI and machine learning in modern biomedical research and healthcare. The book covers foundational concepts in AI/ML, computational biology, and medical informatics, followed by in-depth chapters on medical imaging, network biology, chemoinformatics, and public health. Other sections address ethical and societal implications, interpretable AI, and real-world case studies, making complex topics accessible through clear language and structured content. Designed for early-career researchers, students, and professionals without prior expertise in computer science or health sciences, this book provides a progressive learning path from basic to intermediate levels.
Readers benefit from practical examples, online resources, and a coherent chapter structure that supports both academic study and applied research.
1. 1. Introduction to AI/ML, computational biology, and medical informatics - p. 1 2. 2. AI/ML applications in medical imaging - p. 17 3. 3. Natural language technologies in biomedical domain - p. 41 4. 4. AI/ML in chemoinformatics - p. 73 5. 5. Deep learning methods for network biology - p. 97 6. 6. Probabilistic optimization of ML for heart disease prediction - p. 115 7. 7. The need for interpretable and explainable deep learning data in health care - p. 137 8. 8. Using hybrid models in healthcare data - p. 161 9. 9. Improving disease prediction by integrating multiple ML and optimization techniques - p. 185 10. 10. Ethical, societal, and legal issues in AI/ML for healthcare - p. 207 11. 11. Deep learning in gait abnormality detection: Principles and illustrations - p. 223 12. 12. Broad applications of network embedding in computational biology, genomics, medicine, and health - p. 245 13. 13. AI use in clinical prediction - p. 275 14. 14. AI/ML for medical informatics and public health - p. 289 15. 15. AI in medical imaging for developing countries: Challenges and opportunities - p. 307 16. 16. AI applications in disease diagnosis and treatment: New applications - p. 345 17. Index - p. 363
