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

Introducing deep learning algorithms and Python libraries

DL is an umbrella term that represents the different neural network architectures, along with the libraries for building the same. Unlike ML, the requirements for DL are quite diverse and the vast number of resources can be intimidating for those either looking to get into this field or those who have already been into it. Let’s look at the top DL libraries and how they can be leveraged to build DL models. There are a few DL libraries that are currently available for building DL models, but we will only highlight the most popular and widely used libraries for building a variety of DL models and architectures – TensorFlow, PyTorch, and Keras. In addition, there are genome-specific DL libraries that are also available. It is beyond the scope of this chapter to go into details about each of these DL libraries, but we will provide fundamentals that are sufficient for building DL models on several use cases, as we will...