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 autoencoders?

Autoencoders are a type of deep NN (DNN) that can learn an efficient reduced representation of the data in an unsupervised way and minimize the error between the compressed and subsequently reconstructed data compared to the original data. Why compress the data and then reconstruct the original data? Isn’t it counterintuitive? Suppose you are on your vacation and took pictures, but you realized after you return from vacation that a picture has noise because of dim light. Wouldn't it be nice if there was a way to remove the background and make the picture great? This is, in computer vision lingo, called feature variation, which removes any noise in a picture. This is what autoencoders do. They learn a representation or latent space from the training data to ignore signal noise. The compression step forces the network to only learn the most important latent features. This is because if the model is at full capacity, it will just copy the data without...