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

Genomics Data Analysis

Genomics gained mainstream attention when the Human Genome Project published the complete sequence of the human genome in 2003. Over the last decade, genomics has become the backbone of drug discovery, targeted therapeutics, disease diagnosis, and precision medicine, leading to the chances of successful clinical trials. For example, in 2021, over 33% of FDA-approved new drug approvals were personalized medicines, a trend that sustained for the past five years ( This growing use of genomics can be mainly attributed to the drastic decrease in the cost and turnaround time of DNA sequencing. For instance, while human genome sequencing was reported to cost around $3 billion and took 13 years to complete, today, you can get your genome sequenced in a day with less than $200 (