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

Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics

Deep learning (DL) is the branch of machine learning (ML) that encompasses deep neural networks (DNNs), with many artificial neurons arranged into several layers that mimic the human brain. Recently, DL algorithms have shown impressive results in several domains of life sciences and biotechnology. Even though the application of DL in genomics is relatively new, it has led to a fundamental understanding of biological and cellular processes in living systems. This has prompted many companies to leverage DL to solve important problems such as drug development, treatment of patients, and disease control, ultimately paving the way toward the promise of precision medicine in near future. Genomics is a data-rich discipline and is replete with complex datasets and often ill-understood. DL’s success in genomics is largely attributed to its ability to perform knowledge extraction and pattern recognition from complex...