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


Understanding the key concepts of the DL life cycle—which involves several phases, right from understanding the business problem, all the way to model monitoring—is important for developing DL models for genomic applications because each of these phases is critical for the success of building a highly accurate model. Developing DL models for genomic applications not only involves conceptual understanding but also knowing how to practically apply these algorithms in genomics using the available DL libraries.

We started the chapter by going through the iterative steps of a DL life cycle which start with understanding the business problem and culminating with model monitoring. We understood how DL can help to solve a business problem with the right framing of the business problem into a DL problem. Fortunately, the DL community has come up with detailed steps that can help make this process easy.

Since training, tuning, and evaluation are the main topics of...