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

Monitoring models using advanced tools

So far, you have built and evaluated the CNN TFBS prediction model (Chapter 9, Building and Tuning Deep Learning Models), interpreted it (Chapter 10, Model Interpretability in Genomics), and deployed it successfully (this chapter). You have even ensured that the model is working smoothly and correctly in a production environment. So, you might be thinking you are done, right? Not even close – but this is the beginning of a new journey. Just imagine what could go wrong after model deployment. Models can start to degrade post-deployment and consistently not perform the way they are expected to. Even though you have done everything right from model building to model deployment, things can go wrong after the model goes live in the production environment. Even after you have troubleshooted and tested a model thoroughly, things can still go wrong after model deployment.

Why monitor models?

Theoretically, once a model has been deployed,...