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

Google Colab notebooks: Because we will be training a convolutional NN (CNN) on slightly larger datasets than our laptop CPU could handle, we will develop our models in Google Colab. Colab is a free-to-use Jupyter notebook-kind of framework that provides a free Graphical Processing Unit (GPU) to train models. We will learn the basics of Google Colab in the following section, but readers can find more details about Colab here: The key to building models in Google Colab is selecting a GPU so that you can build your model faster. You just go to the Runtime drop-down menu, select Change Runtime type, and then select GPU in the Hardware accelerator drop-down menu, as shown here:

Figure 9.1 – Google Colab notebook settings

Once a GPU has been selected, you can run the commands shown in the hands-on section of this chapter.