Applying Artificial Intelligence to Computational Biology and Medical Informatics
- 1st Edition - May 1, 2026
- Latest edition
- Editor: Mohammad Sufian Badar
- Language: English
Applying Artificial Intelligence to Computational Biology and Medical Informatics explores the transformative role of AI and machine learning in modern biomedical research and he… Read more
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Applying Artificial Intelligence to Computational Biology and Medical Informatics explores the transformative role of AI and machine learning in modern biomedical research and healthcare. This volume bridges the gap between computational methods and biological applications, offering a comprehensive introduction for students and researchers across disciplines. 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. It also addresses 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.
- Introduces foundational concepts in AI, machine learning, computational biology, and medical informatics, enabling readers from diverse academic backgrounds to build a solid interdisciplinary understanding
- Presents real-world case studies and practical applications of AI/ML in disease diagnosis, treatment, and public health, fostering translational insights and research relevance
- Maintains a coherent pedagogical structure that progresses from basic to intermediate levels, supporting both self-paced learning and integration into academic curricula
researchers in bioinformatics, biomedical sciences, computer science, and related interdisciplinary fields
1. Introduction to AI/ML, Computational Biology, and Medical Informatics
2. AI/ML Applications in Medical Imaging
3. Natural Language Technologies in Biomedical Domain
4. AI/ML in Chemoinformatics
5. Deep Learning Methods for Network Biology
6. Probabilistic Optimization of ML for Heart Disease Prediction
7. The Need for Interpretable and Explainable Deep Learning Data in Health Care
8. Ethical, Societal, and Legal Issues in AI/ML for Healthcare
9. Deep Learning in Gait Abnormality Detection: Principles and Illustrations
10. Broad Applications of Network Embedding in Computational Biology, Genomics, Medicine, and Health
11. AI/ML for Medical Informatics and Public Health
2. AI/ML Applications in Medical Imaging
3. Natural Language Technologies in Biomedical Domain
4. AI/ML in Chemoinformatics
5. Deep Learning Methods for Network Biology
6. Probabilistic Optimization of ML for Heart Disease Prediction
7. The Need for Interpretable and Explainable Deep Learning Data in Health Care
8. Ethical, Societal, and Legal Issues in AI/ML for Healthcare
9. Deep Learning in Gait Abnormality Detection: Principles and Illustrations
10. Broad Applications of Network Embedding in Computational Biology, Genomics, Medicine, and Health
11. AI/ML for Medical Informatics and Public Health
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
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Mohammad Sufian Badar
Dr. M.S Badar, MS, PhD served as a Teaching Faculty in the Department of Bioengineering at the University of California, Riverside, CA, USA. He graduated with an MS degree in Molecular Science and Nanotechnology and Ph.D. in Engineering from Louisiana Tech University Ruston, LA, USA, respectively. Dr. Badar has over 14 years of teaching, research, and industry experience. He has authored a chapter in a book about Machine Learning and Molecular Modeling. He has developed an algorithm for Face Detection, Recognition, and Emotion Recognition. He has developed a device that, by using a Biosensor, can correlate the physiology of the human body with the emotion recognition algorithm, giving us an accurate measurement of stress hormones in the body. His group is developing an ML Model which predicts Covid infection based on the severity of symptoms (mild, moderate, and severe) and if they have had contact with a Covid patient.
Affiliations and expertise
Senior Teaching Faculty, Department of Bioengineering, University of California, Riverside, CA, USA