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


Deep learning is the subset of machine learning based on artificial neural networks with representative learning using vast amounts of data. Machine learning is a subcomponent of artificial intelligence, which includes sophisticated algorithms that enable machines to mimic human intelligence to perform human tasks automatically. Both deep learning and machine learning help automatically detect meaningful patterns from data without explicit programming. Machine learning and deep learning have completely changed the way that we live these days. We rely on these so much that it’s hard to imagine a day without using any of these in some way or another, whether it is via the spam filtering of emails, product recommendations, or speech recognition. Both machine learning and specifically deep learning have been adopted by the scientific community in areas such as biology, genomics, bioinformatics, and computational biology. High-throughput technologies (HTS) such as next-generation sequencing (NGS) have made a significant contribution to genomics to study complex biological phenomena at a single-base-pair resolution on an unprecedented scale, facilitating an era of big data genomics. To get meaningful and novel biological insights from this big data, most of the algorithms are currently based on machine learning and, lately, deep learning methodologies to provide higher levels of accuracy in specific tasks related to genomics than state-of-the-art rule-based algorithms. Given the growing trend in the perception and application of machine learning and deep learning in genomics, research professionals, scientists, and managers require a good understanding of this exciting field to equip them with the necessary tools, technologies, and general guidelines to assist them in the selection of machine learning and deep learning methods for handling genomics data and accelerating data-driven decision-making in industries related to life sciences and biotechnology.

Throughout this book, we will learn how to apply deep learning approaches to solve real-world problems in genomics, interpret biological insights from deep learning models built from genomic datasets, and finally, operationalize deep learning models using open source tools to enable predictions for end users.