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

Types of unsupervised DL

There are broadly three different types of UL methods that are currently available:

  • Clustering
  • Anomaly detection
  • Association

Let’s discuss each of these in detail in the following sections.


Clustering, as the name suggests, is a type of UL method to group similar data points in the training dataset—for example, the clustering of tissues based on the gene expression values from genomics data. This is the most common method of UL. Here, the DL models look for similar data points in the training data to group them using the appropriate distance measurement method (Figure 7.1). One challenge with the clustering method is you need to predefine the number of clusters for the algorithm to group clustering based on the number of clusters. However, there are methods out there that can help arrive at this cluster size to input into the learning algorithm.

Figure 7.1 – Clustering of multiple...