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

Model interpretability methods in genomics

The field of genomics has garnered so much attention lately because of the advances in high-throughput methods, such as next-generation sequencing (NGS), and other omics technologies such as proteomics, metabolomics, and so on. This has resulted in abundant data such that researchers are in a dilemma about how to use this. The DNN methods showed superior performance compared to the state-of-the-art conventional methods in many genomics applications in medical research, especially in imaging tasks, tumor identification, antibody discovery, motif finding, genetic variant detection, and chromatin interaction, to name a few. However, the major complaint from DNN architectures is that they are black-box models. What that means is that we don’t know how these models made decision on a given dataset. To make predictions with a DL model, the input data is passed through several layers of a DNN, each layer containing several nodes that have...