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)
1
Part 1 – Machine Learning in Genomics
5
Part 2 – Deep Learning for Genomic Applications
11
Part 3 – Operationalizing models

Practical applications of GANs in genomics

GANs have found a lot of applications in several domains such as NLP, CV, and genomics because of their ability to produce synthetic data samples to augment the real world and help improve models’ fitness. State-of-the-art synthetic models such as GANs can produce an artificial version of high-dimensional and complex genomic datasets with high accuracy, scale, and privacy. The artificial datasets can be shared among researchers and enable future genomics research and safe, private data sharing between researchers, health care providers, and the industry. As discussed briefly in the introduction, there are several use cases of GANs in genomics such as the automatic design of probe sequences for binding assays, optimization of genomic sequences, creation of synthetic genomes, and so on.

We will now see some examples of how GANs are applied to genomics and solve some real-world problems in the following section.

Analysis of ScRNA...