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


DL has shown great promise in genomics and these methods now match or exceed the current state-of-the-art methods in a diverse array of tasks and disciplines in life sciences and biotechnology. Given this rapid rise in its applicability across broad research areas to understand the complexities of biological systems, its adoption is still low. This can be attributed to several factors, the major among them being the complexity of genomic data. In this final chapter of this book, we have seen some of the challenges and common pitfalls associated with applying DL in genomics, which reduces the effectiveness of these DL methods. Addressing these challenges and pitfalls can be hard because of the complexity of the DL methods. Often, the mistakes are very subtle, and you didn’t know that you are making them. To avoid making these simple mistakes, you must have a deep understanding of the concepts surrounding genomics and DL. One major piece of advice is that if the genomics...