Data Science for Genomics

Häftad, Engelska, 2022

1 688 kr

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Beskrivning

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.

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

Produktinformation

Utgivningsdatum:2022-12-02
Mått:216 x 276 x 19 mm
Vikt:450 g
Format:Häftad
Språk:Engelska
Antal sidor:312
Förlag:Elsevier Science
ISBN:9780323983525

Utforska kategorier

Biokemisk teknik inom Naturvetenskap och teknik
Biomedicinsk teknik inom Medicin

Mer om författaren

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.

Innehållsförteckning

1. Introduction to Data Science2. Toolboxes for Data Scientists3. Machine Learning and Deep Learning: A Concise Overview4. Artificial Intelligence5. Data Privacy and Data Trust6. Visual Data Analysis and Complex Data Analysis7. Big Data programming with Apache Spark and Hadoop8. Information Retrieval and Recommender Systems9. Statistical Natural Language Processing for Sentiment Analysis10. Parallel Computing and High-Performance Computing11. Data Science, Genomics, Genomes, and Genetics12. Blockchain Technology for securing Genomic data13. Cloud, edge, fog, etc., for communicating and storing data for Genome14. Open Issues, Challenges and Future Research Directions towards Data science and Genomics15. Privacy Laws16. Ethical Concerns17. Self-study questions18. Problem-based learning19. Key Terms/ Glossary20. Appendix – Keeping up to Date21. Bibliography