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


RNNs are a special type of neural network that is well suited for sequential data such as time series, audio, video, and text. Research showed that RNNs have improved the performance of sequential data types when compared to other architectures such as FNNs and CNNs. The key to an RNN is the sequence memory state, which helps it store information from the previously analyzed state; this is good for sequential signal analysis and predictive analysis. In this chapter, we learned how RNNs are different from FNNs and CNNs. We understood the different types of RNNs and what makes them good for sequential data analysis by looking at a few examples. RNNs, as you may have noticed, are good for mapping a fixed or variable-sized input sequence to a fixed or variable-sized output; we have seen several examples to understand this.

We also looked at how RNNs can help with genomics tasks and understood the different architectural types of RNNs. Bidirectional RNN, LSTM, and GRU are variants...