Intelligent Data Analytics for Bioinformatics and Biomedical Systems (Sustainable Computing and Optimization)

The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems.

Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases.

This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges.

Audience

Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data.

Neha Sharma PhD, is an assistant professor in the Department of Computer Science and Engineering, Chitkara University, Rajpura, India. She has more than 60 international publications in reputed peer-reviewed journals. She has also published more than 30 national & international patents under the Intellectual Property Rights of the governments of India and abroad. Her main areas of research are in image processing, machine learning, deep learning, and cybersecurity.

Korhan Cengiz, PhD, is an assistant professor at the Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Kralove, Czech Republic. He obtained his doctorate in electronics engineering from Kadir Has University, Istanbul, Turkey, in 2016 and has authored more than 40 SCI articles, five international patents, ten chapters in books, and one book. His research interests include wireless sensor networks, wireless communications, statistical signal processing, etc.

Prasenjit Chatterjee, PhD, is a professor of mechanical engineering and dean (research and consultancy) at MCKV Institute of Engineering, West Bengal, India. He has authored several books on intelligent decision-making, fuzzy computing, supply chain management, etc. He has over 6850 citations and many research papers in various international journals. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) and Ranking Alternatives through Functional Mapping of Criterion Sub-Intervals into a Single Interval (RAFSI).