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

GANs for Improving Models in Genomics

One of the significant developments in the field of Deep learning (DL) has been the introduction of new generative models. The most popular generative models are Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), deep autoregressive models, style transfer, and so on. We learned about what VAEs are in the previous chapter. GANs have become a hot topic in the DL research community in the last few years. They were introduced by Ian Goodfellow in 2014 and are considered one of the most interesting ideas of the last 10 years by Yann LeCun, who is considered the father of modern DL. A GAN, as the name suggests, is a type of generative model that is trained in an adversarial setting to learn data distribution that is closer to the real world, thereby generating synthetic data inexpensively. GANs have revolutionized many domains such as natural language processing (NLP), computer vision (CV), and, most recently, genomics because of...