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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

Machine Learning Methods for Genomic Applications

Have you ever wondered how YouTube recommends videos to you, banks detect fraudulent activity and send notifications to you, or Gmail filters the spam messages from your inbox? These are just a few examples of how the world of business is currently using machine learning (ML). The field of ML has impacted numerous areas of modern society and is responsible for some of the most significant improvements in technologies such as self-driving cars, exploring the galaxy, predictions for disease outbreaks, and so on. The enormous growth in ML is primarily driven by its huge success in solving real-world business problems in healthcare, finance, e-commerce, agriculture, life sciences, pharmaceuticals, and biotechnology. The life sciences and biotechnology industries are huge and diverse with many subsectors. Very popular fields are drug discovery and manufacturing, therapeutics, diagnostics, genomics, and so on.

The field of genomics has...

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