Book Image

Deep Learning for Genomics

By : Upendra Kumar Devisetty
Book Image

Deep Learning for Genomics

By: Upendra Kumar Devisetty

Overview of this book

Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you’ll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you’ll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.
Table of Contents (18 chapters)
Part 1 – Machine Learning in Genomics
Part 2 – Deep Learning for Genomic Applications
Part 3 – Operationalizing models

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 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.


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