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 Interpretability in Genomics

Deep learning (DL) methods have been widely adopted in genomics for extracting biological insights and model predictions because of their superior performance in predictions and classification tasks through their deep neural network (DNN) architecture. Even though the accuracy and efficiency of these model predictions are the primary goals of DL in genomics applications, the decisions made by these DNNs is also important in genomics toward the goal of understanding cellular and molecular mechanisms. In Machine Learning (ML) and DL, "Model interpretability" refers to how easy it is for humans to understand the decisions made by the model. The more interpretable the models are, the easier is it to understand the model's decisions. In contrast, difficulties in model interpretation limit the practical utility of DL models and reduce confidence in their adoption. However, it’s not easy to interpret DL model behavior in a way that...