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

Introducing model deployment

When you start a DL project, your primary focus should be on data collection, data processing, and model development. The process of model development from data collection to model training always happens in an offline setting. However, wouldn’t it be nice if the awesome model that you worked so hard on turned into something that other people can interact with? Even great models won’t have much impact if they remain in the notebooks. Also, you wouldn’t run your notebook every time new data comes in, right? So, how do you do it? The simple answer is model deployment, which is the process of integrating the model into an existing production environment to make appropriate business decisions based on data. It is the second-to-last stage of the DL life cycle before model monitoring and is the most important step for solving business challenges. A model that’s deployed in production has several advantages:

  • It will serve the...