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

Introducing Convolutional Neural Networks for Genomics

In recent years, deep learning (DL) has emerged as a prominent technology in solving complex problems in various domains. Among DL algorithms, convolutional neural networks (CNNs) dominate the current DL applications because of their incredible accuracy in computer vision (CV) and natural language processing (NLP) tasks. A CNN is a type of neural network (NN) architecture that is used for unstructured data and was originally designed to fully automate the classification of handcrafted characters. Some popular applications of CNNs include facial recognition, object detection, self-driving cars, auto-translation, handwritten character recognition, X-ray image analysis, cancer detection, biometric authentication, and so on. Compared to feed-forward NNs (FNNs), which we learned about in the previous chapter, CNNs process multiple arrays using convolutions within a local field, like perceiving images by eye. Thanks to next-generation...