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


A successful application of DL for a genomics problem heavily relies not only on developing an accurate model but also on how to make the model impactful. Model deployment is the process of transitioning a trained model built on notebooks into a production environment where it is used for prediction, classification, clustering, and other purposes. Unlike model training, deploying models requires different skills that are not traditionally taught to data scientists and other genomic scientists because these skills, such as web app development, cloud computing, and working with APIs, are more software development skills. As the boundaries between data scientists and MLEs become blurred, knowledge of model deployment will take researchers a long way. In this chapter, you were introduced to a simple workflow for deploying the built model using some open source and easy-to-implement tools. These tools are easy to use and allow you to deploy a web app that can predict TFBS in a quick...