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

Recurrent Neural Networks in Genomics

Deep learning (DL) models are so versatile that they can adapt to any input data distribution and, at the same time, generalize very well to previously unseen data. A variety of deep neural network (DNN) architectures have been designed to suit a particular task. For example, we saw how feedforward neural networks (FNNs) are good at making predictions from structured data, such as tabular data, in Chapter 4, Deep Learning for Genomics. We also saw how convolutional neural networks (CNNs) are good at making predictions from unstructured data such as images, audio, text, and DNA sequence data; we saw this in Chapter 5, Introducing Convolutional Neural Networks for Genomics. But what about sequential data? If you look around, we are currently flooded with a lot of sequential data. Some examples include financial data and DNA sequences. The most important type of sequential data is the time series data, which is a series of data points listed in time...