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

Applications of CNNs in genomics

Now that you understand how CNNs work in genomics with a simple example, let’s look at some of their applications that are popular in genomics.


TFs are DNA- and RNA binding proteins that play a crucial role in gene regulation. Knowing the binding sites of these DNA- and RNA-binding proteins would help us to develop models and can help identify disease-causing variants. One way to infer the sequence specificities of these proteins is through position weight matrices (PWMs), which can be used to scan the entire genome to identify potential binding sites of these DNA- and RNA-binding proteins. In addition, DNA- and RNA-binding protein specificities are measured by several high-throughput assays such as PBM, SELEX, and the most popular ChIP- and CLIP-seq techniques. Some of the challenges associated with this data are that the raw data comes in several different quantitative forms, the data is often extremely huge, and each data generation...