Readers benefit from practical examples, online resources, and a coherent chapter structure that supports both academic study and applied research.
Table of Contents
1. Introduction to AI/ML, Computational Biology, and Medical Informatics2. 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. Using hybrid models in healthcare data
9. Improving Disease Prediction by Integrating Multiple ML and Optimization Techniques
10. Ethical, Societal, and Legal Issues in AI/ML for Healthcare
11. Deep Learning in Gait Abnormality Detection: Principles and Illustrations
12. Broad Applications of Network Embedding in Computational Biology, Genomics, Medicine, and Health
13. AI Use in Clinical Prediction
14. AI/ML for Medical Informatics and Public Health
15. AI in Medical Imaging for Developing Countries: Challenges and Opportunities
16. AI Applications in Disease Diagnosis and Treatment: New Applications
Authors
Mohammad Sufian Badar Senior Teaching Faculty, Department of Bioengineering, University of California, Riverside, CA, USA.Mohammad Sufian Badar, PhD, is an Assistant Professor in the Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi. Previously, he was a Senior Teaching Faculty at UC Riverside, CA, USA, and an Analytics Architect at CenturyLink in Denver, CO, USA. He holds an MS in Molecular Science and Nanotechnology and a PhD in Engineering from Louisiana Tech University. He earned an MSc in Bioinformatics from Jamia Millia Islamia, New Delhi. With many years of teaching, research, and industry experience, Dr. Badar has published in conferences and journals, authored chapters on AI, machine learning, blockchain, IoT, and computational biology and has edited and authored several books in his area of interest like AI, ML, computational biology, and the integration of AI/ ML with health sciences.
