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 for Genomics

Recently, there has been a rapid increase in interest in genomics-based applications in the biomedical, pharmaceutical, and therapeutics industries. Machine learning (ML), with its sophisticated mathematical and data analysis techniques, coupled with advances in next-generation sequencing (NGS) have played a huge role in this rapid rise. As most genomic companies and other research organizations started to produce genomic data to keep themselves ahead of the curve, the ability to extract novel biological insights and build predictive models from this ever-growing data has proved to be a challenge for ML because it relied on hand-crafted features for model training and predictions as we saw in the previous two chapters. Translating this massive genomic data from an incomprehensible resource into meaningful insights automatically and intuitively requires more expressive ML models and algorithms.

Deep learning (DL), a subcategory of ML that can extract features...