Data Science for Genomics

Data Science for Genomics

1st Edition - November 27, 2022

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  • Editors: Amit Kumar Tyagi, Ajith Abraham
  • Paperback ISBN: 9780323983525
  • eBook ISBN: 9780323985765

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Description

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. Sections cover 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 key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. 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. 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.

Key Features

  • Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics
  • Presents a roadmap of future trends suitable for innovative Data Science research and practice
  • Includes topics such as Blockchain technology for securing data at end user/server side
  • Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns

Readership

Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science and Engineering, Biomedical Engineering, Biology, Chemistry, Genomics, and Information Technology. The audience also includes interested professionals-experts from both public and private industries of biomedical, genomics, computer science, data science, and information technology; The book may be used in Data Science, Medical, Biomedical, Artificial Intelligence, Machine Learning, Deep Learning oriented courses given at especially Health, Biology, Biomedical Engineering, Genetics or similar programs of universities, institutions

Table of Contents

  • 1. Introduction to Data Science
    2. Toolboxes for Data Scientists
    3. Machine Learning and Deep Learning: A Concise Overview
    4. Artificial Intelligence
    5. Data Privacy and Data Trust
    6. Visual Data Analysis and Complex Data Analysis
    7. Big Data programming with Apache Spark and Hadoop
    8. Information Retrieval and Recommender Systems
    9. Statistical Natural Language Processing for Sentiment Analysis
    10. Parallel Computing and High-Performance Computing
    11. Data Science, Genomics, Genomes, and Genetics
    12. Blockchain Technology for securing Genomic data
    13. Cloud, edge, fog, etc., for communicating and storing data for Genome
    14. Open Issues, Challenges and Future Research Directions towards Data science and Genomics
    15. Privacy Laws
    16. Ethical Concerns
    17. Self-study questions
    18. Problem-based learning
    19. Key Terms/ Glossary
    20. Appendix – Keeping up to Date
    21. Bibliography

Product details

  • No. of pages: 312
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: November 27, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323983525
  • eBook ISBN: 9780323985765

About the Editors

Amit Kumar Tyagi

Dr. Amit Kumar Tyagi is Assistant Professor and Senior Researcher at Vellore Institute of Technology (VIT), Chennai, India. His current research focuses on Machine Learning with Big Data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart and Secure Computing and Privacy. He has contributed to several projects such as “AARIN” and “P3-Block” to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems. He received his Ph.D. Degree from Pondicherry Central University, India. He is a member of IEEE.

Affiliations and Expertise

Senior Assistant Professor and Senior Researcher, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

Ajith Abraham

Dr. Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. The Network with HQ in Seattle, USA has currently more than 1,500 scientific members from over 105 countries. As an Investigator / Co-Investigator, he has won research grants worth over 100+ Million US$. Currently he works as a Professor of Artificial Intelligence in Innopolis University, Russia and is a Chairholder of the Yayasan Tun Ismail Mohamed Ali Professorial Chair in Artificial Intelligence of UCSI, Malaysia. Dr. Abraham works in a multi-disciplinary environment and he has authored / coauthored more than 1,400+ research publications out of which there are 100+ books covering various aspects of Computer Science. One of his books was translated to Japanese and few other articles were translated to Russian and Chinese. Dr. Abraham has more than 45,500+ academic citations (h-index of 100 as per google scholar). He has given more than 150 plenary lectures and conference tutorials (in 20+ countries). Since 2008, Dr. Abraham was the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (which has over 200+ members) during 2008-2021 and served as a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). Dr. Abraham was the editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) during 2016-2021 and is currently serving / served the editorial board of over 15 International Journals indexed by Thomson ISI. Dr. Abraham received Ph.D. degree in Computer Science from Monash University, Melbourne, Australia (2001) and a Master of Science Degree from Nanyang Technological University, Singapore (1998).

Affiliations and Expertise

Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, United States

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