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

Understanding what deep learning is and how it works

In the past few years, ML has been the go-to tool for academic research and industries since ML made it possible to learn complicated functions and patterns from highly complex data without human intervention. As early as 1980, theoretical results such as Universal Approximation Theorem seemed to indicate that it may be possible for a neural network to learn any function that existed in a dataset. This is a powerful approach because there are several problems in the real world that traditional methods cannot solve. This led to the birth of DL. Even though DL has been around for about a decade now, it has gotten mainstream attention recently. So, why didn’t DL take off until recently? This can be mainly attributed to the lack of DL frameworks, big data, and efficient hardware to build complex DL models until recently. It’s only been possible to use DL to produce meaningful empirical results due to the introduction of...