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


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


activation function 60

binary step function 60

leaky ReLU 62

ReLu 61

sigmoid 62

softmax 63, 64

tanh 63

activator protein (AP) 175

advanced tools, model monitoring

data quality tools 222

DL monitoring tools 223

monitoring 222

system monitoring tools 222

AI, in genomics market

reference link 6

Amazon SageMaker 210

Amazon Web Services (AWS) 17

Analysis using Denoising Autoencoders for Gene Expression (ADAGE)

reference link 130

anomaly detection 119

novelty detection 120

outlier detection 119, 120

area under the curve (AUC) 172

artificial neural networks (ANNs) 4

artificial neuron (AN) 57

association 121

autoencoder applications, for predicting gene expression


gene expression clustering, boosting 131