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 is a relatively new area, with most publications happening in the last few years, but it is a very active area of research in DL now that is of utmost importance to realize the promise of precision medicine. The ability to interpret model decisions or predictions has several business advantages and can ultimately lead to higher profits. Because of model interpretability, more and more companies are leaning toward using DL models in their decision-making processes. This is not restricted to low-risk sectors but also high-risk sectors such as medicine and genomics too. If they are not currently using model interpretability, they plan to incorporate it into their future strategy.

This chapter is an attempt to introduce you to model interpretability, why it is important, why business organizations care about it, and different methods for performing model interpretability, specifically for black-box models such as DNNs in the genomics field. The chapter...