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

Deep learning challenges regarding genomics

The recent explosion of genomics data due to the advancements in next-generation sequencing (NGS) coupled with improvements in omic technologies (transcriptomics, proteomics, and metabolomics) has led to a greater understanding of the biological process of the living cell. Meanwhile, the remarkable success of DL based on DNN, has brought enormous improvements in computer vision (CV), natural language processing (NLP), and machine translation, and this has attracted the attention of genomics. The field of genomics quickly leveraged these specialized neural network architectures that can perform various tasks, such as binding site identification using CNNs, improving code optimization for improved protein translation through RNNs, unsupervised DL through autoencoders to predict gene expression, and so on. This is particularly exciting because genomics requires a data-driven and sophisticated solution to extract meaningful biological insights...