Data Science for Genomics

by Amit Kumar Tyagi PhD. (Editor), Ajith Abraham PhD (Editor)

Overview

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.

Product Details

ISBN-13:	9780323983525
Publisher:	Elsevier Science
Publication date:	12/02/2022
Pages:	312
Product dimensions:	8.50(w) x 10.88(h) x (d)

About the Author

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.

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.

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

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From the Publisher

Presents the key concepts and methods of data science applied to genomics, including real-world case studies and examples