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.
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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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Presents the key concepts and methods of data science applied to genomics, including real-world case studies and examples
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
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Produktdetaljer
ISBN
9780323983525
Publisert
2022-12-02
Utgiver
Elsevier Science & Technology; Academic Press Inc
Vekt
450 gr
Høyde
276 mm
Bredde
216 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
312