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)
1
Part 1 – Machine Learning in Genomics
5
Part 2 – Deep Learning for Genomic Applications
11
Part 3 – Operationalizing models

Common pitfalls for applying deep learning to genomics

The genomics field has undergone a big data revolution with the advent of NGS, which has allowed researchers to take molecular measurements such as gene expression at a genomic scale. This technological advancement has led to a greater understanding of cellular and biological processes and has shown promise for treating many uncurable diseases in clinical settings. As the amount and complexity of genomic data increased, researchers started to leverage DL to extract useful biological information and build predictive models. This has led to many DL tools being used for a wide variety of genomic analysis tasks, such as processing raw data, integrating heterogeneous datasets, predictive modeling, and so on. To prevent low model performance when applying DL for genomics data, there are common pitfalls that one should be aware of. Let’s discuss the common pitfalls that you might face when trying to apply DL to genomic tasks and...