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


This chapter introduced you to DL, a subcategory of ML that leverages artificial neural networks to mimic human brains and perform automated tasks without human intervention. DL has certainly come to the fore in the last few years because of the incredible advancements in the availability of big data, sophisticated algorithms, and improvements in computational hardware such as CPUs and GPUs. We started this chapter by understanding why there is a need for sophisticated algorithms to mine insights from ever-growing genomics data and how DL, using DNNs, can fill that gap. The anatomy of the neural network architecture, along with the key components of neural networks, was introduced. Understanding these key concepts is important to be able to build a solid foundation for DL concepts, as well as understand how they relate to genomic applications. Then, you were introduced to the different neural network architectures, such as CNNs, RNNs, GANs, GNNs, and autoencoders, and understood...