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

To get the most out of this book

The book aims to keep it self-contained as possible. To extract the maximum value out of this book, a basic to intermediate knowledge of Python programming is recommended and a background in genomics, statistics, and bioinformatics and some knowledge of data science is a must. In addition, readers are expected to know the basics of machine learning and associated machine learning algorithms, such as regression and classification. The book provides a hands-on approach to implementation and associated deep learning methodologies that will have you up-and-running and productive in no time. At the end of the book, you will be able to put your knowledge to work with this practical guide.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository. This will ensure you avoid any potential error related to copying and pasting of code.