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

Best practices for applying deep learning to genomics

So far, we have seen several challenges with DL for genomics and common pitfalls and how to avoid them. To make the best use of DL for genomics, let’s look at a collection of some best practices to follow while leveraging DL for genomics.

Understand the problem and know your data better

As seen in the DL life cycle, the most important aspect of DL is understanding the business goal or scientific question that you are trying to solve and then framing the business goal into a DL problem. Having a proper understanding of the scientific question (business goal) and a clear analysis plan (framing the business goal into a DL problem) are key to the success of DL projects in genomics. You should not even start working on DL without defining the goals of the project. For instance, would you step into the lab without thinking about what you plan to do that day? No. Right? Some of the key questions that you should be asking to...