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


This chapter started with what ML is and how ML algorithms can help genomic applications through their inherent nature of uncovering hidden patterns in the dataset, automating human tasks, and making predictions on unseen data. We looked at the several types of ML algorithms—namely, supervised and unsupervised methods—and understood the main steps in ML methods. Then, we understood the ML workflow for genomic applications.

In the second half of the chapter, we spent quite a bit of time understanding the different steps in ML and what is involved in each step of the workflow. We also introduced the most popular Python packages Pandas and scikit-learn to work on the ML workflow. Finally, we worked on a real-world application of ML on a genomic dataset for identifying the disease state of cancer patients.

This chapter and the preceding chapters are meant for a quick primer on ML for genomics, and with this knowledge and understanding of fundamentals, in the...