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


DL has made massive strides in several domains of life sciences and biotechnology, including genomics. CNN architecture is mainly designed for unstructured data. It accepts the input image or a DNA sequence (matrix of size x ) as an input, extracts the features from the image, and does the prediction or classification through a series of hidden layers such as a convolutional layer, a pooling layer, a non-linear fully connected layer, and an output layer. CNNs do not require any separate feature extraction step and automatically derive features from the input data. CNNs have revolutionized the field of genomics because of their incredible accuracy and ability to process unstructured data, which is quite common in genomics.

In this chapter, we have looked at the history of CNNs, what they are, and the different components of CNN architecture. Later in the chapter, we understood how CNNs are being leveraged in genomics for studying complex problems such as gene expression...