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

Applications and use cases of RNNs in genomics

Even though FNNs and CNNs are extremely popular for tackling problems in genomics, they both have limitations. Genomics is all about sequence data, so RNNs can play a key role in several genomics applications. In addition, RNNs can find long-range dependencies in the data, which is why RNNs are great for genomic applications. RNNs are currently being used in several genomics applications, such as constructing a genotype imputation and phenotype sequences prediction system, base-calling accuracy for nanopore sequencing data, genetic regulatory networks, predicting protein functions, and so on. We’ll quickly look at a few RNN-based applications in genomics in the following section.


DeepNano is a freely available base caller for the Oxford Nanopore Technology (ONT) sequencing platform ( Base calling is particularly important for sequencing platforms...