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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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Toc

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

Technical requirements

Let’s understand the technical requirements for the different Python packages and other ML libraries that are needed to apply ML in genomics in this chapter.

Python packages

The following are some common Python packages that every data scientist and genomic researcher uses for not only genomic analysis for any kind of data analysis.

Pandas

Pandas is one of the most popular data analysis tools in Python. Pandas do not need an introduction as it is part and parcel of every data scientist’s tool. The great thing about Pandas is it contains all the functions and methods to support data analysis irrespective of the type of data. It’s also super easy to install Pandas, which you can do by simply entering pip install pandas in your terminal. Then, you can include import pandas as pd in your Python script, which you will see later in the chapter.

Matplotlib

We will be using Matplotlib, a very popular Python library for visualization...

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