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

Use case – Predicting the binding site location of the JunD TF

In the last section of the chapter, we will see how to leverage a DNN algorithm to solve the problem of prediction of Transcription Factor Binding Site (TFBS) predictions in the human genome. We will build a DL model using one of the most popular NN architectures that are commonly used in genomics: CNNs, which we learned about previously. But before that, let’s understand the problem and data in detail.

TFs are proteins that control gene regulation. They bind to the regulatory regions of the DNA such as Promoters and either promote or repress gene expression. Each TF has a specific binding motif that it binds to, which is referred to as a TFBS. The identification of a TFBS is very challenging because the binding motifs are generally very small (<10bp) or not completely specific, or a TF may bind to many similar but not identical sequences, or in some cases, some bases in the motifs are generally more...