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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

Applications and use cases of RNNs in genomics

Even though FNNs and CNNs are extremely popular for tackling problems in genomics, they both have limitations. Genomics is all about sequence data, so RNNs can play a key role in several genomics applications. In addition, RNNs can find long-range dependencies in the data, which is why RNNs are great for genomic applications. RNNs are currently being used in several genomics applications, such as constructing a genotype imputation and phenotype sequences prediction system, base-calling accuracy for nanopore sequencing data, genetic regulatory networks, predicting protein functions, and so on. We’ll quickly look at a few RNN-based applications in genomics in the following section.

DeepNano

DeepNano is a freely available base caller for the Oxford Nanopore Technology (ONT) sequencing platform (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178751). Base calling is particularly important for sequencing platforms...

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