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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

Data processing

In the DL life cycle, the data (inputs and outputs) serves important functions such as defining the goal of the problem, training the algorithm, evaluating the performance of the trained model, and building baselines for model monitoring and so this is considered as the most important phase of the DL life cycle. The data processing phase can be subdivided into data collection and data wrangling, which in turn divides into data processing and feature engineering, as depicted here:

Figure 9.3 – Different subphases of data preprocessing

As shown here, the data collection phase mainly includes identifying data resources and the accessibility of data. The data wrangling phase includes data preprocessing and feature engineering. Let’s discuss each of the phases in detail in the following section.

Data collection

Data collection is technically the first step of the DL life cycle. Without data, there is no model. Data collection...

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