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

Technical requirements

In this chapter, we will discuss some tools and software that are essential for model deployment and model monitoring. Let’s go over the technical specifications.


Streamlit ( is an open source framework for building web apps based on Python. It is a faster way to build and share web apps in minutes using Python, which fits in very well with DL frameworks such as Keras. The main advantage of Streamlit compared to other frameworks is it is easy to build, quick to deploy a model in a cloud environment, and previous knowledge of the frontend is not required. It is best suited for data scientists or genomic researchers who are not web developers and they don’t need to spend their time learning web development to build apps. Streamlit enables them to quickly build a web app and share it with their collaborators, which they can run to make predictions or classifications without any knowledge of DL. Unlike other frameworks...