Bridging the critical gap between complex genomic data and actual clinical practice, this essential volume delivers the cutting-edge AI methodologies, expert bioinformatics insights, and practical case studies needed to unlock truly personalized medicine.
Modern biological computation requires both increasing speed and accuracy for applications ranging from genomics to personalized medicine. As the need for this technology grows, classical computing methods of tackling problems start to become unsustainable, both at a scalability and energy level. This book investigates the convergence of quantum computing, bioinformatics, and environmental sustainability for modern applications. It argues that bioinformatics and biomedical discovery will not just be quantum-fast, but ecologically sustainable and morally framed. The book contends that although quantum computing offers exponential acceleration of data analysis and modeling in life sciences, these advantages would need to be built with deliberate consideration of energy expenses, hardware sustainability, and broader impacts on society. Using an organized, cross-disciplinary framework of how quantum bioinformatics can be built and implemented sustainably, this volume examines how quantum computing’s unprecedented capabilities will revolutionize bioinformatics and biomedical innovation. The book serves as an invitation to action and an instruction manual for scientists, technologists, and policymakers poised to inform the future of medicine in an era of quantum possibility.
Raghavendra M. Devadas, PhD is an Assistant Professor at the Manipal Institute of Technology at the Manipal Academy of Higher Education, Bengaluru, Karnataka, India. He has published two books, filed two patents, and received two grants. His areas of interest include machine learning, software engineering, fuzzy logic, and databases.
Preethi, PhD is an Assistant Professor in the Department of Information Technology at the Manipal Institute of Technology with more than 17 years of teaching experience. She has more than 35 publications in international journals and conferences of repute. Her research interests include computer architecture, IoT, cybersecurity, and image processing.
Praveen Gujjar, PhD is an Associate Professor and Area Head of Business Analytics in the CMS Business School at Jain University with more than 16 years of teaching experience. He has authored numerous publications in reputed journals, holds 86 patents, and has secured major research grants from multiple entities. He specializes in data visualization, predictive analytics, and prescriptive analytics.
Sowmya T., PhD is an Assistant Professor at the Manipal Institute of Technology. She received her Ph.D. from Christ University in Bengaluru. Her current research interests include network security.
Yashaswini K.A., PhD is an experienced academician with more than 16 years of teaching experience in Computer Science and Engineering. She serves as an Assistant Professor at the Manipal Institute of Technology in Bengaluru under the Manipal Academy of Higher Education. Her areas of expertise include artificial intelligence, machine learning, and data analytics.
Bharti Jagwani Motwani, PhD is an Academic Director of the Online Master of Science and Business Analytics and a Clinical Associate Professor in the Robert Smith College of Business at the University of Maryland. She is the sole author of many books related to machine learning and artificial intelligence. Her research focuses on machine and deep learning.
