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

Different RNN architectures

Despite the incredible success of RNNs in solving some of the hardest problems in sequential modeling and predictions, the limitation with RNNs is that they put so much emphasis on the most recent inputs. This means that the last input you feed into the network has a higher influence on the prediction compared to the previous timestamps. . When the values of the gradients are too small making model converge slower, this is what we call “memory” loss or the vanishing gradient problem when the values of the gradients are too small making model converge slower. RNNs are not good at remembering long-term associations in the data. Another issue with the vanilla RNN is that it only used information earlier in the sequence. To address the limitations of RNNs, several variations of RNN architectures have been proposed, such as bidirectional RNN, long short-term memory (LSTM), and gated recurrent unit (GRU). We will briefly look at these popular RNN...