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

Unlocking business value from model interpretability

When it comes to predictive modeling, there is a trade-off between wanting to know what is predicted or why a prediction was made, or you do not care why a decision was made. Each of those scenarios depends on the use case. For example, if you are building a model for research and development (R&D) purposes, then you would sacrifice model interpretation over accuracy, whereas if you are building a model for business use, it is important to understand how the model is making that decision. Either way, it is important to know the model’s behavior not only to understand why some decisions were made but also for debugging and model improvement.

Let’s now understand why model interpretability for DL is important and how it helps to unlock practical business benefits such as better business decisions, building trust, and increasing profitability in the following section.

Better business decisions

Before model...