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

Data processing

In the DL life cycle, the data (inputs and outputs) serves important functions such as defining the goal of the problem, training the algorithm, evaluating the performance of the trained model, and building baselines for model monitoring and so this is considered as the most important phase of the DL life cycle. The data processing phase can be subdivided into data collection and data wrangling, which in turn divides into data processing and feature engineering, as depicted here:

Figure 9.3 – Different subphases of data preprocessing

As shown here, the data collection phase mainly includes identifying data resources and the accessibility of data. The data wrangling phase includes data preprocessing and feature engineering. Let’s discuss each of the phases in detail in the following section.

Data collection

Data collection is technically the first step of the DL life cycle. Without data, there is no model. Data collection...