Skip to main content
Elsevier

Data Science for Genomics

  • 1st Edition - November 27, 2022
  • Latest edition
  • Editors: Amit Kumar Tyagi, Ajith Abraham
  • Language: English

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and model… Read more

Data Mining & ML

Unlock the cutting edge

Up to 20% on trusted resources. Build expertise with data mining, ML methods.

Explore now

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

  • Edition: 1
  • Latest edition
  • Published: December 2, 2022
  • Language: English

About the editors

AT

Amit Kumar Tyagi

Amit Kumar Tyagi is an Assistant Professor, at the National Forensic Sciences University, Gandhinagar, Gujarat, 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.
Affiliations and expertise
Assistant Professor, National Institute of Fashion Technology, New Delhi, India

AA

Ajith Abraham

Ajith Abraham, PhD (2001) in artificial intelligence from Monash University, Australia, is the Vice Chancellor of Sai University, India. Prior to joining Sai University, he held senior positions at several prominent institutions and was the founding director of Machine Intelligence Research Labs, a nonprofit scientific network for innovation and research excellence headquartered in Seattle, United States. Dr. Abraham has led or co-led research projects valued at over $110 million, funded by organizations in the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in multidisciplinary environments for more than 35 years and is a prolific co-author of research publications in artificial intelligence and its industrial applications. Several of his works have been translated into Chinese and Russian, and one of his books has been translated into Japanese. Dr. Abraham is consistently featured in the Stanford/Elsevier list of the world’s top 2% most-cited scientists. From 2016 to 2021, he served as Editor-in-Chief of Engineering Applications of Artificial Intelligence (EAAI), one of the longest-standing journals in the field (founded in 1988). He has also served on the editorial boards of more than 15 international journals indexed by Thomson ISI.

Affiliations and expertise
Vice Chancellor and Dean, School of Artificial Intelligence, Sai University, Chennai, Tamil Nadu, India

View book on ScienceDirect

Read Data Science for Genomics on ScienceDirect