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

Analysis of genomic data

Genomic data analysis aims to provide biological interpretation and insights from genomics data and help drive innovation. It is like any other data analysis with the exception that it requires domain-specific knowledge and tools. With the advances in NGS technologies, it is estimated that genomic research will generate significant amounts of data in the next decade. However, our ability to mine insights from this big data is lagging behind the pace at which the current data is being generated. As new and more high-throughput genomic data is getting generated, data analysis capabilities are sought-after features for researchers and other scientific professionals in both academia and industry.

Steps in genomics data analysis

Genomics data is generally complex in nature and size. Researchers are currently facing an exciting yet challenging time with this available data that needs to be analyzed and understood. Analyzing this big genomics data can be extremely...