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

Unsupervised Deep Learning with Autoencoders

Over the past few years, data-driven deep learning (DL) approaches have made impressive progress in the genomics field. The development of high-throughput technologies such as next-generation sequencing (NGS) has played a major part in this data-driven revolution. Several neural network (NN) architectures have found success in the genomics domain. For instance, in the previous chapters, we have seen feed-forward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), which have been successfully used for many genomics applications. So far, all these NN architectures require that you have well-labeled data. However, a lack of ground truth and accurate labels is common in the genomics domain, which limits the application of supervised learning (SL) methods. NGS has significantly increased the use of gene expression assays, and there is so much genomics data out there with no label. Several methods...