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

What are RNNs?

Before we understand RNNs, let’s refresh our memory and revisit how FNNs and CNNs work. In a typical FNN, you have an input layer, multiple hidden layers, and an output layer. After all the data is fed into the input layer, the information passes to the hidden layer. Then, the dot product of the input value and weight of each node is summed up, along with the bias term, which is turned into an activation function at each of the three nodes (Figure 6.1). The activation function can be binary, sigmoid, ReLu, LeakyReLu, or something else, as you learned in Chapter 4, Deep Learning for Genomics. Depending on the type of activation function, the value of the single node in the hidden layer is outputted:

Figure 6.1 – A multi-dimensional input type FNN

The number of nodes in the output layer depends on the problem and the required output. For example, if you are trying to classify a DNA sequence based on mutations in each of the 10 different...