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

Model Deployment and Monitoring

The primary goal of Machine Learning (ML) and Deep Learning (DL) is to build predictive models and get insights from the data to help solve business problems. However, a trained model can only do that once you take your model and turn it into a production environment – a process referred to as model deployment. A deployed model enables other researchers (either within your organization or outside) to interact with and extract the most value out of it. Many models don’t end up in the production environment purely because of technical challenges. Once a model is put into the production environment, constant attention is required on an ongoing basis to detect any drifts or anomalies for the success of a DL project – a process referred to as model monitoring. Model deployment and model monitoring are two of the most important challenges that a lot of researchers face after they build their models.

The skills and expertise that are...