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Data science for genomics /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Academic Press,
2022.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Data science for genomics / |c edited by Amit Kumar Tyagi, Ajith Abraham. |
| 264 | 1 | |a Amsterdam : |b Academic Press, |c 2022. | |
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| 520 | |a 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. | ||
| 505 | 0 | |a 1. Introduction to Data Science<br>2. Toolboxes for Data Scientists<br>3. Machine Learning and Deep Learning: A Concise Overview<br>4. Artificial Intelligence<br>5. Data Privacy and Data Trust<br>6. Visual Data Analysis and Complex Data Analysis<br>7. Big Data programming with Apache Spark and Hadoop<br>8. Information Retrieval and Recommender Systems<br>9. Statistical Natural Language Processing for Sentiment Analysis<br>10. Parallel Computing and High-Performance Computing<br>11. Data Science, Genomics, Genomes, and Genetics<br>12. Blockchain Technology for securing Genomic data<br>13. Cloud, edge, fog, etc., for communicating and storing data for Genome<br>14. Open Issues, Challenges and Future Research Directions towards Data science and Genomics<br>15. Privacy Laws<br>16. Ethical Concerns<br>17. Self-study questions<br>18. Problem-based learning<br>19. Key Terms/ Glossary<br>20. Appendix -- Keeping up to Date<br>21. Bibliography | |
| 650 | 0 | |a Genomics |x Data processing. | |
| 650 | 7 | |a Genomics |x Data processing. |2 fast |0 (OCoLC)fst00940229 | |
| 700 | 1 | |a Tyagi, Amit Kumar, |e editor. | |
| 700 | 1 | |a Abraham, Ajith, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 9780323985765 |
| 776 | 0 | 8 | |i Print version: |z 0323983529 |z 9780323983525 |w (OCoLC)1311358595 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780323983525 |z Texto completo |