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This book comprises eight chapters covering various aspects of healthcare. Initial chapters provide the basics of AI and molecular biology required to understand the more advanced topics. Later chapters cover key important aspects of bioinformatics such as modelling paradigms for biological processes, synthetic biology, and drug discovery, including case studies related to topics including cancer, ocular disease, and mental health. The book is relevant and timely.
Artificial Intelligence (AI) is a very promising area of research and there is considerable interest in its application to a wide range of issues in the healthcare sector. However, many users of this technology do not have a good grasp of the key underlying principles of this technology. Although there are several resources already available in this area providing necessary content, they tend to be over-simplified, and demonstrate how to use applications, rather than the underlying principles. This book is intended to contribute as both a textbook and a demonstration of the way in which the AI algorithms can be applied. It will address the drawbacks in some of the current resources, as well as emphasize the benefits of the use of AI in this important field.
Sommario
Chapter 1. From Data Science to Artificial Intelligence.- Chapter 2. Molecular Biology Lite.- Chapter 3. Application of Deep Learning to Healthcare.- Chapter 4. Computational modeling paradigms in systems biology.- Chapter 5. Image Analysis and Cancer Detection.- Chapter 6. Genomics and Synthetic Biology.- Chapter 7. Drug Discovery and Drug Repurposing.- Epilogue (about 7-10 pages long).
Info autore
Dr Madhu Chetty, as an academic at Federation University Australia, serves as Professor in Information Technology within the Institute of Innovation, Science and Sustainability (IISS), and as the Director of AI and ML Stream within Health Innovation and Transformation Centre (HITC). He has over 40 years of tertiary teaching, research, and leadership experience within Australia and overseas. He has a prolific publication record, several visiting appointments at various international universities, and has supervised over 25 doctoral students. He has been recipient of several grants and awards including Australia-India Strategic Research Fund, award for excellence in research supervision, and commendation for community engagement. He has served as general chair of IEEE international conferences, vice chair of the IEEE Victorian/Tasmanian Section, deputy head of school at Monash University Australia, and editorial board member of high impact journals.
Dr Jennifer Hallinan has degrees in both molecular biology and computer science, and combined these into a PhD in computational biology. She has researched and taught at Universities in both Australia and the United Kingdom, working on bioinformatics and synthetic biology and worked at Harvard Medical School on biological network analysis. She has authored over 60 papers published in peer-reviewed journals, and supervised over 20 PhD and Masters students. She was an advisor for three iGEM teams at Newcastle University, UK, all of which won gold medals. She is active in the IEEE, being Program Chair for the 2021 International Conference on Bioinformatics and Computational Biology, and serves as Vice-Chair for Education and Outreach of the IEEE Computational Intelligence Society.
Riassunto
This book comprises eight chapters covering various aspects of healthcare. Initial chapters provide the basics of AI and molecular biology required to understand the more advanced topics. Later chapters cover key important aspects of bioinformatics such as modelling paradigms for biological processes, synthetic biology, and drug discovery, including case studies related to topics including cancer, ocular disease, and mental health. The book is relevant and timely.
Artificial Intelligence (AI) is a very promising area of research and there is considerable interest in its application to a wide range of issues in the healthcare sector. However, many users of this technology do not have a good grasp of the key underlying principles of this technology. Although there are several resources already available in this area providing necessary content, they tend to be over-simplified, and demonstrate how to use applications, rather than the underlying principles. This book is intended to contribute as both a textbook and a demonstration of the way in which the AI algorithms can be applied. It will address the drawbacks in some of the current resources, as well as emphasize the benefits of the use of AI in this important field.
