Data Science for Genomics - Softcover

Über die Autorinnen und Autoren

Amit Kumar Tyagi is an Assistant Professor at the National Forensic Sciences University, Gandhinagar, India. He previously held academic and research positions, including at Vellore Institute of Technology (2019–2022), and earned his PhD from Pondicherry Central University in 2018.

He has supervised doctoral and master’s students and contributed to research projects on privacy and security in vehicular applications and medical cyber-physical systems. He has published over 200 peer-reviewed papers and holds more than 25 patents in areas such as deep learning, the Internet of Things, cyber-physical systems, and computer vision.

He has edited over 25 books and authored four books in related fields, and has received multiple research awards. His interests include machine-based communications, blockchain, and secure, privacy-preserving computing. He is a member of ACM and IEEE.

Dr. Ajith Abraham is the Pro Vice-Chancellor for Academics, Research, Incubation, and International Relations at Bennette University. He is also the Founding Director of Machine Intelligence Research Labs (MIR Labs), a global non-profit scientific network that connects academia and industry to support research and innovation. He is also serving as Vice Chancellor of Sai University, Chennai.

His research interests include artificial intelligence and machine intelligence, cyber-physical systems, the Internet of Things (IoT), network security, Web intelligence, sensor networks, and data mining. He serves as Chair of the IEEE Systems, Man, and Cybernetics Society Technical Committee on Soft Computing and has held editorial leadership roles, including Editor-in-Chief of Engineering Applications of Artificial Intelligence.
Dr. Abraham earned his Ph.D. in Computer Science from Monash University, Australia.

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Data Science for Genomics presents the foundational concepts of Data Science as they pertain to Genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. The authors begin by presenting an introduction to Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes, and Proteomes as basic concepts of molecular biology, along with DNA and the key features of the human genome, as well as the genomes of eukaryotes and prokaryotes in general. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR, that were used in the pre-genomics era to examine individual genes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell are presented, as well as the important issue of how chromatin structure influences genome expression. Readers will learn about the assembly of the transcription initiation complexes of prokaryotes and eukaryotes, along with a detailed discussion of DNA-binding proteins, these playing the central roles in the initial stages of genome expression. Each aspect of Genomics is aligned with associated Data Science concepts and methods including Machine Learning, Deep Learning, Artificial Intelligence, Data Privacy and Data Trust, Visual Data Analysis and Complex Data Analysis, Big Data Programming with Apache Spark and Hadoop, Blockchain technology for securing Genomic data, Cloud, Edge and Fog computing, as well as future research directions.