BIOINFORMATICS AND MEDICAL APPLICATIONS
The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology.
Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician’s important tools and examines how they are used to evaluate biological data and advance disease knowledge.
The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information.
Audience
The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.
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A. Suresh, PhD is an associate professor, Department of the Networking and Communications, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India. He has nearly two decades of experience in teaching and his areas of specialization are data mining, artificial intelligence, image processing, multimedia, and system software. He has published 6 patents and more than 100 papers in international journals.
S. Vimal, PhD is an assistant professor in the Department of Artificial Intelligence & DS, Ramco Institute of Technology, Tamilnadu, India. He is the editor of 3 books and guest-edited multiple journal special issues. He has more than 15 years of teaching experience.
Y. Harold Robinson, PhD is currently working in the School of Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has published more than 50 papers in various international journals and presented more than 70 papers in both national and international conferences.
Dhinesh Kumar Ramaswami, BE in Computer Science, is a Senior Consultant at Capgemini America Inc. He has over 9 years of experience in software development and specializes in various .net technologies. He has published more than 15 papers in international journals and national and international conferences.
R. Udendhran, PhD is an assistant professor, Department of Computer Science and Engineering at Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India. He has published about 20 papers in international journals.
The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology.
Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician’s important tools and examines how they are used to evaluate biological data and advance disease knowledge.
The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information.
Audience
The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
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