Data Science for Genomics

Amit Kumar Tyagi , Ajith Abraham 지음

Elsevier 2023년 03월 22일 출간 종이책 : 2022년 12월 09일 출간

작품소개

책소개

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

목차

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

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작가정보

저자(글) Amit Kumar Tyagi

저자(글) Ajith Abraham