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


The application of unsupervised DL through learned representation is becoming extremely popular in genomics because of the large-scale datasets produced using NGS technologies. Autoencoders are being routinely used by researchers because of their promise and success across many genomics applications. Autoencoders learn by the reduced representation of the data through compression and reconstruction. During the process, they learn the key features of the data and identify the data structure automatically from examples rather than through handcrafting by humans. Diverse types of autoencoders exist to ensure that the reduced representation of the data identifies the key attributes of the original data. Autoencoders have several applications in genomics, mainly in gene expression analysis. With tools such as ADAGE, autoencoders are helping genomics datasets with no labels get biological insights from that data. We started the chapter by understanding what is unsupervised learning...