Apply deep learning algorithms to solve real-world problems in the field of genomics
Extract biological insights from deep learning models built from genomic datasets
Train, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomics
Book Description
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.
What you will learn
Discover the machine learning applications for genomics
Explore deep learning concepts and methodologies for genomics applications
Understand supervised deep learning algorithms for genomics applications
Get to grips with unsupervised deep learning with autoencoders
Improve deep learning models using generative models
Operationalize deep learning models from genomics datasets
Visualize and interpret deep learning models
Understand deep learning challenges, pitfalls, and best practices
Who this book is for
This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.