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

Supervised and unsupervised ML

The goal of ML is to develop and deploy computational algorithms that can automatically learn and improve from experience without human interference to perform a particular task. But how does it work? It does so by first “learning” knowledge from experience from the input data and using that knowledge to make predictions on unseen data. As such, the crux of ML is the learning problem in which machines learn from real-world data, improve from experience, extract patterns, construct models, and predict the outcomes of unseen data.

Depending on the type of data and the tasks to perform, ML algorithms can be broadly divided into supervised, semi-supervised, and unsupervised methods. Supervised methods learn patterns from examples with labels (for example, “diseased” or “not diseased”) and are then used to predict future events or labels from unseen data (Figure 3.1). Unsupervised methods, in contrast, don’...