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

What are GANs?

Before we discuss GANs, you should know how generative models work. But before that, it would be advisable to understand how generative models are different from discriminative models.

Differences between Discriminative and Generative models

DL models can be broadly divided into discriminative models and generative models. Simply put, discriminative models focus on generating predictions of labels from the features mainly used for supervised learning (SL), and generative models focus on explaining how the data is generated and are used for unsupervised learning (UL). Let’s go into this a little deeper to understand the differences.

Discriminative models try to find the relationships between , such as features, and , such as targets. For example, if you are trying to predict the cancer type from genomic variations (single nucleotide polymorphisms, or SNPs), the here indicates the features of those data instances such as the number of variations, type...