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

Building and Tuning Deep Learning Models

Deep learning (DL) algorithmic solutions are currently being leveraged in several biological and life sciences domains to address some of the most challenging problems in healthcare, medicine, agriculture, genomics, and so on. Among these disciplines, genomics poses extreme challenges to DL because of how complex the field of genomics is, which goes way beyond the knowledge of how to interpret genomes. Thankfully, a lot of genomics research with DL has led to the design of sophisticated deep neural networks (DNN) architectures that are suited to genomic tasks. This intersection of DL with genomics proved very successful, leading to the application of DL to several genomic applications in regulatory genomics, functional genomics, structural genomics, and so on. Furthermore, it allowed the genomics research community to gain a global perspective of the human genome, which is paving the way for the goal of genomic medicine in near future.