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

Introducing RNNs

To address the limitations of FNNs and CNNs regarding sequential data, we need a network that can meet 2 requirements.

  1. It can takes sequences of non-fixed lengths, one element of the sequence at a time.
  2. It must not only identify the nonlocal relationships in the sequence but also remember the most important events that happened before.

This idea led to the development of RNNs, which are a variant of DNN with a feedback loop (hidden state) that can feed the results back into the network and make them part of the final output (Figure 6.3):

Figure 6.3 – Recurrent neural network

RNNs capture previous observations or historical events up to the current timestamp and because the hidden state of the current stamp is the same as the previous timestamp, the computation is recurrent (hence why they are referred to as RNNs):

Figure 6.4 – Difference between a standard neural network (a) and a recurrent...