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

What is model interpretability?

Deep learning's popularity is mainly because of the sophisticated algorithms such as DNNs it uses to perform complex tasks. If trained popularly, the models are not only accurate but also generalize very well on real-world data. DL, with its ability to extract novel insights using automatic feature extraction and to identify complex relationships in massive datasets showed superior performance compared to the state-of-the-art conventional methods. The promise of these DL models, however, comes with some limitations. With their black-box kind of nature, these DL models face problems in explaining the relationship between inputs and predicted outputs or, in other words, model interpretability. It is humanly impossible to follow the reasoning for a particular prediction using these black-box models. You might be wondering if the DL models perform and generalize well, why you would not trust the model that it is making the right decisions. There can...