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 big data

Genomics is the study of the function, structure, and evolution of genomes in living organisms. A genome is the blueprint of an organism that has a complete set of DNA, including genes and other intergenic regions. Genes are the basic components of DNA, and they play an important role in inheritance. The field of genomics got mainstream attention after the completion of human genome sequencing in 2003. The human genome project catapulted the field of genomics and it transformed medicine, giving birth to the modern biotechnology industry. Genomics got another push with the introduction of Next-Generation Sequencing (NGS) in the early 2000s, which enabled researchers and scientists to generate massive amounts of data, leading to scientific breakthroughs.

Genomic data has gained a lot of attention in the last decade because of the incredible progress it has made in precision genomics, genomic medicine, drug development, therapeutics, and so on. For example, since...