Data Science for Genomics - Softcover

Über die Autorinnen und Autoren

Amit Kumar Tyagi is an Assistant Professor, at the National Institute of Fashion Technology, New Delhi, India. Previously he worked as an Assistant Professor (Senior Grade 2), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India from 2019-2022. He received his Ph.D. Degree (Full-Time) in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009 to 2010, and 2012 to 2013. He was an Assistant Professor and head researcher at Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), India from 2018 to 2019. He supervised one PhD thesis and more than ten Master dissertations. He has contributed to several projects such as “AARIN” and “P3- Block” to address some of the open issues related to privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber-Physical Systems (MCPS). He has published over 200 papers in refereed high-impact journals, conferences, and books, and some of his articles won best paper awards. Also, he has filed more than 25 patents (Nationally and Internationally) in the areas of Deep Learning, Internet of Things, Cyber-Physical Systems, and Computer Vision. He has edited more than 25 books for IET, Elsevier, Springer, CRC Press, etc. Additionally, he has authored 4 Books on Intelligent Transportation Systems, Vehicular Ad-hoc Network, Machine learning and Internet of Things, with IET UK, Springer Germany, and BPB India publisher. He won the Faculty Research Award of the Year for 2020, 2021, and 2022 consecutively, given by Vellore Institute of Technology, Chennai, India. Recently, he was awarded the best paper award for his paper “A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient”, in ICCSA 2020, Italy (Europe). His current research focuses on Next Generation Machine Based Communications, Blockchain Technology, Smart and Secure Computing and Privacy. He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, Universal Scientific Education and Research Network, CSI, and ISTE.

Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.

Von der hinteren Coverseite

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.