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Deep Learning for Genomics

You're reading from   Deep Learning for Genomics Data-driven approaches for genomics applications in life sciences and biotechnology

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804615447
Length 270 pages
Edition 1st Edition
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Author (1):
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Upendra Kumar Devisetty Upendra Kumar Devisetty
Author Profile Icon Upendra Kumar Devisetty
Upendra Kumar Devisetty
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Table of Contents (18) Chapters Close

Preface 1. Part 1 – Machine Learning in Genomics
2. Chapter 1: Introducing Machine Learning for Genomics FREE CHAPTER 3. Chapter 2: Genomics Data Analysis 4. Chapter 3: Machine Learning Methods for Genomic Applications 5. Part 2 – Deep Learning for Genomic Applications
6. Chapter 4: Deep Learning for Genomics 7. Chapter 5: Introducing Convolutional Neural Networks for Genomics 8. Chapter 6: Recurrent Neural Networks in Genomics 9. Chapter 7: Unsupervised Deep Learning with Autoencoders 10. Chapter 8: GANs for Improving Models in Genomics 11. Part 3 – Operationalizing models
12. Chapter 9: Building and Tuning Deep Learning Models 13. Chapter 10: Model Interpretability in Genomics 14. Chapter 11: Model Deployment and Monitoring 15. Chapter 12: Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics 16. Index 17. Other Books You May Enjoy

Genomics big data

Genomics is the study of the function, structure, and evolution of genomes in living organisms. A genome is the blueprint of an organism that has a complete set of DNA, including genes and other intergenic regions. Genes are the basic components of DNA, and they play an important role in inheritance. The field of genomics got mainstream attention after the completion of human genome sequencing in 2003. The human genome project catapulted the field of genomics and it transformed medicine, giving birth to the modern biotechnology industry. Genomics got another push with the introduction of Next-Generation Sequencing (NGS) in the early 2000s, which enabled researchers and scientists to generate massive amounts of data, leading to scientific breakthroughs.

Genomic data has gained a lot of attention in the last decade because of the incredible progress it has made in precision genomics, genomic medicine, drug development, therapeutics, and so on. For example, since...

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