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

CNNs for genomics

Even though CNNs are primarily used for unstructured data such as images, text, audio, and so on, they are also powerful tools for non-image data such as DNA. Unfortunately, the raw DNA sequence data cannot be provided to CNNs as input for feature extraction. It has to be converted to numerical representation before it can be used by CNN. The first thing to note for non-numeric data such as a DNA sequence is that you will have to first convert the 1D DNA sequence data to a one-hot encoded structure (Figure 5.8):

Figure 5.8 – Example of one-hot encoding for a DNA sequence

As shown in the preceding diagram, each nucleotide in the DNA sequences is represented as a one-hot vector: A = [1000], C = [0100], G = [0001], and T = [0010]. The one-hot encoded matrix can then be fed into the model for training purposes. Please note that one-hot encoding is not the only way of representing DNA sequences to a CNN. There is also label encoding in which...