Data Science for Genomics

Herausgeber: Abraham, Ajith; Tyagi, Amit Kumar

• Broschiertes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Technology
• Seitenzahl: 312
• Erscheinungstermin: 2. Dezember 2022
• Englisch
• Abmessung: 216mm x 275mm x 19mm
• Gewicht: 856g
• ISBN-13: 9780323983525
• ISBN-10: 0323983529
• Artikelnr.: 63870701

Inhaltsangabe

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