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 algorithms have seen a major upgrade recently with the development of generative models such as VAEs and GANs, which contributed significantly to the creation of synthetic datasets. With this development, the fields of CV, NLP, and genomics have profited immensely. In the last chapter on Unsupervised learning using Autoencoders, you were introduced to VAE and in this chapter, you were introduced to GANs and how they can be used to address some of the limitations of genomics data and, improve DL models. First, we looked at the differences between discriminative and generative models, and then next we understood the key components of GANS which are the generator and discriminator, how they are trained and constantly pit against each other in an adversarial way to generate synthetic data as close as possible to real-world data.

Because of GANs ability to generate synthetic data and DL’s requirement for a large amount of data, we see how GANs are used for improving...