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

An ML use case for genomics – Disease prediction

Let’s illustrate the power of ML for genomic applications, starting with classification models, which are a subset of supervised ML methods where the goal is to classify the outcome into two (binary classification) or more (multiclass classification) classes based on the independent variables.

One of the popular use cases for genomics is outcome prediction. In this particular use case, we will try to predict if a patient has lung cancer or not based on gene expression. Before we start building the model and using that to make a prediction, let’s try to understand how a typical ML disease prediction model work in this use case. It works by mapping the relationships between individual patients’ sample gene expression values (features) and the target variable (Normal versus Tumor)—in other words, mapping the pattern of the features within the expression data to the target variable. In this example,...