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

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781804615447

Length 270 pages

Edition 1st Edition

Concepts

Deep Learning

Author (1):

Upendra Kumar Devisetty

View More author details

Table of Contents (18) Chapters

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

Why subscribe?

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...

The rest of the chapter is locked